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Links to many research areas in the department may be found at http://www.cs.princeton.edu/research/areas/ while links to projects may be found at http://www.cs.princeton.edu/research/projects/ .
How to Contact Faculty for IW/Thesis Advising
Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.
Computer Science Faculty:
Ryan Adams - Available for single-term IW and senior thesis advising, 2022-2023
Andrew Appel - Available for single-term IW and senior thesis advising, 2022-2023
Sanjeev Arora - Not available for single-term IW or senior thesis advising, 2022-2023
David August - Available for single-term IW and senior thesis advising, 2022-2023
Mark Braverman - Available for single-term IW and senior thesis advising, 2022-2023
Bernard Chazelle - Available for single-term IW and senior thesis advising, 2022-2023
Danqi Chen - Available for single-term IW and senior thesis advising, 2022-2023
Jia Deng - Available for single-term IW and senior thesis advising, 2022-2023
Adji Dieng - Available for single-term IW and senior thesis advising, 2022-2023
Robert Dondero - Available for single-term IW and senior thesis advising, 2022-2023
Zeev Dvir - Available for single-term IW and senior thesis advising, 2022-2023
Christiane Fellbaum - Available for single-term IW and senior thesis advising, 2022-2023
Adam Finkelstein - Not available for single-term IW or senior thesis advising, 2022-2023
Robert S. Fish - Available for single-term IW and senior thesis advising, 2022-2023
Michael Freedman - Available for single-term IW and senior thesis advising, 2022-2023
Ruth Fong - Available for single-term IW and senior thesis advising, 2022-2023
Tom Griffiths - Available for single-term IW and senior thesis advising, 2022-2023
Aarti Gupta - Available for single-term IW and senior thesis advising, 2022-2023
Elad Hazan - Available for single-term IW and senior thesis advising, 2022-2023
Felix Heide - Available for single-term IW and senior thesis advising, 2022-2023
Kyle Jamieson - Available for single-term IW and senior thesis advising, 2022-2023
Alan Kaplan - Available for single-term IW and senior thesis advising, 2022-2023
Brian Kernighan - Available for single-term IW and senior thesis advising, 2022-2023
Zachary Kincaid - Available for single-term IW and senior thesis advising, 2022-2023
Gillat Kol - Available for single-term IW and senior thesis advising, 2022-2023
Amit Levy - Available for single-term IW and senior thesis advising, 2022-2023
Dan Leyzberg - Available for single-term IW and senior thesis advising, 2022-2023
Kai Li - Available for single-term IW and senior thesis advising, 2022-2023
Xiaoyan Li - Available for single-term IW and senior thesis advising, 2022-2023
Wyatt Lloyd - Available for single-term IW and senior thesis advising, 2022-2023
Jérémie Lumbroso - Available for single-term IW and senior thesis advising, 2022-2023
Margaret Martonosi - Available for single-term IW and senior thesis advising, 2022-2023
Jonathan Mayer - Available for single-term IW and senior thesis advising, 2022-2023
Andrés Monroy-Hernández - Available for single-term IW and senior thesis advising, 2022-2023
Christopher Moretti - Available for single-term IW and senior thesis advising, 2022-2023
Karthik Narasimhan - Available for single-term IW and senior thesis advising, 2022-2023
Arvind Narayanan - Not available for single-term IW or senior thesis advising, 2022-2023
Pedro Paredes - Available for single-term IW and senior thesis advising, 2022-2023
Iasonas Petras - Available for single-term IW and senior thesis advising, 2022-2023
Yuri Pritykin - Available for single-term IW and senior thesis advising, 2022-2023
Benjamin Raphael - Available for single-term IW and senior thesis advising, 2022-2023
Ran Raz - Available for single-term IW and senior thesis advising, 2022-2023
Jennifer Rexford - Available for single-term IW and senior thesis advising, 2022-2023
Szymon Rusinkiewicz - Available for single-term IW and senior thesis advising, 2022-2023
Olga Russakovsky - Available for single-term IW and senior thesis advising, 2022-2023
Sebastian Seung - Available for single-term IW and senior thesis advising, 2022-2023
Jaswinder Pal Singh - Available for single-term IW and senior thesis advising, 2022-2023
Mona Singh - Available for Fall 2022 IW advising only
Robert Tarjan - Available for single-term IW and senior thesis advising, 2022-2023
Olga Troyanskaya - Available for single-term IW and senior thesis advising, 2022-2023
David Walker - Available for single-term IW and senior thesis advising, 2022-2023
Kevin Wayne - Available for single-term IW and senior thesis advising, 2022-2023
Matt Weinberg - Available for single-term IW and senior thesis advising, 2022-2023
Ryan Adams, Room 411
Available for single-term IW and senior thesis advising, 2022-2023
Research areas:
- Machine learning driven design
- Generative models for structured discrete objects
- Approximate inference in probabilistic models
- Accelerating solutions to partial differential equations
- Innovative uses of automatic differentiation
- Modeling and optimizing 3d printing and CNC machining
Andrew Appel, Room 209
- Research Areas: Formal methods, programming languages, compilers, computer security.
- Software verification (for which taking COS 326 / COS 510 is helpful preparation)
- Game theory of poker or other games (for which COS 217 / 226 are helpful)
- Computer game-playing programs (for which COS 217 / 226)
- Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)
Sanjeev Arora, Room 407
Not available for IW or thesis advising, 2022-2023
- Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
- Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487).
- Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages.
- Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
- Any topic in theoretical computer science.
David August, Room 221
- Research Areas: Computer Architecture, Compilers, Parallelism
- Containment-based approaches to security: We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system. Let's explore ways to use containment to solve real problems. Expect to work with corporate security and technology decision-makers.
- Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures. Let's find ways to realize this parallelism.
- Any other interesting topic in computer architecture or compilers.
Mark Braverman, 194 Nassau St., Room 231
- Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
- Topics in computational and communication complexity.
- Applications of information theory in complexity theory.
- Algorithms for problems under real-life assumptions.
- Game theory, network effects
- Mechanism design (could be on a problem proposed by the student)
Bernard Chazelle, 194 Nassau St., Room 301
- Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms.
- Natural algorithms (flocking, swarming, social networks, etc).
- Sublinear algorithms
- Self-improving algorithms
- Markov data structures
Danqi Chen, Room 412
- My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
- Representation learning for text and knowledge bases
- Pre-training and transfer learning
- Question answering and reading comprehension
- Information extraction
- Text summarization
- Any other interesting topics related to natural language understanding/generation
Jia Deng, Room 423
- Research Areas: Computer Vision, Machine Learning.
- Object recognition and action recognition
- Deep Learning, autoML, meta-learning
- Geometric reasoning, logical reasoning
Adji Dieng, Room 406
- Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.
Robert Dondero, Corwin Hall, Room 038
- Research Areas: Software engineering; software engineering education.
- Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
- In particular, can code critiquing tools help students learn about software quality?
Zeev Dvir, 194 Nassau St., Room 250
- Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
- Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.
Christiane Fellbaum, 1-S-14 Green
- Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
- Natural Language Processing
- Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
- Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
- Quantitative approaches to theoretical linguistics questions
- Extensions and interfaces for WordNet (English and WN in other languages),
- Applications of WordNet(s), including:
- Foreign language tutoring systems,
- Spelling correction software,
- Word-finding/suggestion software for ordinary users and people with memory problems,
- Machine Translation
- Sentiment and Opinion detection
- Automatic reasoning and inferencing
- Collaboration with professors in the social sciences and humanities ("Digital Humanities")
Adam Finkelstein, Room 424
- Research Areas: computer graphics, audio.
- Note: No individual single-term advising is available outside of the seminar for Fall 2021
Robert S. Fish, Corwin Hall, Room 037
- Networking and telecommunications
- Learning, perception, and intelligence, artificial and otherwise;
- Human-computer interaction and computer-supported cooperative work
- Online education, especially in Computer Science Education
- Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
- Distributed autonomous organizations and related blockchain technologies
- Help Future Computer Science Students Learn Computer Science (Remotely)
- Invention and Innovation -- Entrepreneurial Lessons for Computer Scientists (After?) Extraordinary Times
Michael Freedman, Room 308
- Research Areas: Distributed systems, security, networking
- Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at http://sns.cs.princeton.edu/ for current research projects.
Ruth Fong, Room 032
- Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
- Develop a technique for understanding AI models
- Design a AI model that is interpretable by design
- Build a paradigm for detecting and/or correcting failure points in an AI model
- Analyze an existing AI model and/or dataset to better understand its failure points
- Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
- Develop a software package for explainable AI
- Adapt explainable AI research to a consumer-facing problem
Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.
Tom Griffiths, Room 405
Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence
Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.
Aarti Gupta, Room 220
- Research Areas: Formal methods, program analysis, logic decision procedures
- Finding bugs in open source software using automatic verification tools
- Software verification (program analysis, model checking, test generation)
- Decision procedures for logical reasoning (SAT solvers, SMT solvers)
Elad Hazan, Room 409
- Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
- Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
- Implementation and algorithm engineering for control, reinforcement learning and robotics
- Implementation and algorithm engineering for time series prediction
Felix Heide, Room 410
- Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
- Optical Neural Networks
- Hardware-in-the-loop Holography
- Zero-shot and Simulation-only Learning
- Object recognition in extreme conditions
- 3D Scene Representations for View Generation and Inverse Problems
- Long-range Imaging in Scattering Media
- Hardware-in-the-loop Illumination and Sensor Optimization
- Inverse Lidar Design
- Phase Retrieval Algorithms
- Proximal Algorithms for Learning and Inference
- Domain-Specific Language for Optics Design
Kyle Jamieson, Room 306
- Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
- See other topics on my independent work ideas page (campus IP and CS dept. login req'd)
Alan Kaplan, 221 Nassau Street, Room 105
Research Areas:
- Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
- Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
- Software visualization tools for education
- Connected consumer devices, applications and protocols
Brian Kernighan, Room 311
- Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
- Application-oriented languages, scripting languages.
- Tools; user interfaces
- Digital humanities
Zachary Kincaid, Room 219
- Research areas: programming languages, program analysis, program verification, automated reasoning
- Independent Research Topics:
- Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
- Design a domain-specific programming language, or prototype a new feature for an existing language.
- Any interesting project related to programming languages or logic.
Gillat Kol, Room 316
Amit levy, room 307.
- Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
- Distributed hardware testing infrastructure
- Second factor security tokens
- Low-power wireless network protocol implementation
- USB device driver implementation
Dan Leyzberg, Corwin Hall, Room 034
- Research Areas: Computer Science Education, Learning Sciences
- Independent Research Topics: Please contact me if you have an interest in using computing to model the knowledge or skills of others. My favorite projects to advise are those that examine misconceptions learners have with highly technical material, but I'm open to working with you on any project that treats people as individuals and aims to understand more about their frame of reference or way of thinking.
Kai Li, Room 321
- Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
- Fast communication mechanisms for heterogeneous clusters.
- Approximate nearest-neighbor search for high dimensional data.
- Data analysis and prediction of in-patient medical data.
- Optimized implementation of classification algorithms on manycore processors.
Xiaoyan Li, 221 Nassau Street, Room 104
- Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
- Explore new statistical retrieval models for document retrieval and question answering.
- Apply AI in various fields.
- Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
- Any interesting project related to AI, machine learning, and data analysis.
Wyatt Lloyd, Room 323
- Research areas: Distributed Systems
- Caching algorithms and implementations
- Storage systems
- Distributed transaction algorithms and implementations
Jérémie Lumbroso, Corwin Hall, Room 035
- Research areas: Probabilistic algorithms (data streaming algorithms & random generation), data analysis, data structures, analysis of algorithms , analytic combinatorics .
- Develop new algorithms for the distinct sampling problem (useful to in data analysis to get fast representative histograms of a large set of data).
- Extend existing universal random generation framework (such as Boltzmann Sampling , demo here ), or improve their implementation.
- Design an optimized algorithm for a specific combinatorial class.
- Analyze an algorithm using precise analytic combinatorics.
- Text (or data) clustering and processing; linguistic analysis (especially with French, German, Spanish, etc.).
- Design/improve an OMR (Optical Mark Recognition) project that is currently being deployed for the computer assisted grading of exams.
- Integrate handwritten character recognition to the OMR component.
- Analyze large quantities of secondary data collected (for example, do students that do the programming assignments in pairs do better in the course or not? how many hours in COS Lab are helpful on average, and when do we hit a point of diminishing returns).
- Design heuristics and interfaces to spot students in difficulty much earlier in the term, when there is some hope of helping them.
- Extend the COS Lab Queue so it may be used out of the box in all labs accross campus.
- Develop a robust testing infrastructure using virtual machines, secure threads, and intelligent feedback to supplant the run-script system currently used.
- Develop an interface to make grading of assignment done online.
- Integrate hardware solutions (cardswipe, barcode scanning, etc.) to many of these tools to make them even more frictionless.
- Some related crowd-sourcing projects...
- A lot of these projects can include some Big Data component, and involve analyzing data and drawing some observations from it.
- Finally, I am always up for any ambitious coding project, or survey project in preparation (or not) to an undergrad thesis.
Margaret Martonosi, Room 208
- Quantum Computing research, particularly related to architecture and compiler issues for QC.
- Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
- Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
- Other topics in computer architecture or mobile / IoT systems also possible.
Jonathan Mayer, Sherrerd Hall, Room 307
- Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
- Assessing the effects of government policies, both in the public and private sectors.
- Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
- Developing new tools to improve government processes and offer policy alternatives.
Andrés Monroy-Hernández, Room 405
- Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
- Research interests:developing public-interest socio-technical systems. We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems. We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
- Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.
Christopher Moretti, Corwin Hall, Room 036
- Research areas: Distributed systems, high-throughput computing, computer science/engineering education
- Expansion, improvement, and evaluation of open-source distributed computing software.
- Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
- Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
- Sports analytics and/or crowd-sourced computing
Karthik Narasimhan, Room 422
- Research areas: Natural Language Processing, Reinforcement Learning
- Autonomous agents for text-based games ( https://www.microsoft.com/en-us/research/project/textworld/ )
- Transfer learning/generalization in NLP
- Techniques for generating natural language
- Model-based reinforcement learning
Arvind Narayanan, 308 Sherrerd Hall
Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy
Pedro Paredes, Corwin Hall, Room 041
Available for single-term IW and senior thesis advising, 2022-23
My primary research work is in Theoretical Computer Science.
* Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.
The IW projects I am interested in advising can be divided into three categories:
1. Theoretical research
I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.
A (non exhaustive) list of topics of projects I'm interested in: * Explicit constructions of better vertex expanders and/or unique neighbor expanders. * Construction deterministic or random high dimensional expanders. * Pseudorandom generators for different problems. * Topics around the quantum PCP conjecture. * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.
2. Theory informed practical implementations of algorithms Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.
Some examples of areas of interest: * Streaming algorithms. * Numeric linear algebra. * Property testing. * Parallel / Distributed algorithms. * Online algorithms. 3. Machine learning with a theoretical foundation
I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.
One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.
Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.
Iasonas Petras, Corwin Hall, Room 033
- Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
- Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
- Possible research topics include:
1. Quantum algorithms and circuits:
- i. Design or simulation quantum circuits implementing quantum algorithms.
- ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).
2. Information Based Complexity:
- i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case).
- ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
- iii. Necessary and sufficient conditions for tractability of Weighted problems.
- iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.
3. Topics in Scientific Computation:
- i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)
Yuri Pritykin, 245 Carl Icahn Lab
- Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
- Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.
Benjamin Raphael, Room 309
- Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
- Implementation and application of algorithms to infer evolutionary processes in cancer
- Identifying correlations between combinations of genomic mutations in human and cancer genomes
- Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
- Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory
Ran Raz, Room 240
- Research Area: Computational Complexity
- Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science
Jennifer Rexford, Room 222
- Research areas: networking, software-defined networks, network management
- Enterprise and data-center networking solutions built on Software Defined Networking (SDN). For example, middleboxes like firewalls, NATs, intrusion detection systems, and load balancers, adaptive measurement of network traffic, networking in challenged environments (e.g., developing regions, emergency situations, etc.).
- Research on better programming abstractions for SDN. Projects could combine computer networking with other areas like programming languages, network optimization, algorithms, and distributed systems.
- Any interesting project in computer networking.
Szymon Rusinkiewicz, Room 406
- Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
- Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
- Investigate approaches to robust 3D scan matching
- Model and compensate for imperfections in 3D printing
- Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.
Olga Russakovsky, Room 408
- Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
- Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
- Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
- Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model
Sebastian Seung, Princeton Neuroscience Institute, Room 153
- Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
- Gamification of neuroscience (EyeWire 2.0)
- Semantic segmentation and object detection in brain images from microscopy
- Computational analysis of brain structure and function
- Neural network theories of brain function
Jaswinder Pal Singh, Room 324
- Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
- Develop a startup company idea, and build a plan/prototype for it.
- Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
- Study and develop methods to infer insights from data in different application areas, from science to search to finance to others.
- Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
- Design and implement a scalable distributed algorithm.
Mona Singh, Room 420
Available for Fall 2022 IW advising only
- Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
- Whole and cross-genome methods for predicting protein function and protein-protein interactions.
- Analysis and prediction of biological networks.
- Computational methods for inferring specific aspects of protein structure from protein sequence data.
- Any other interesting project in computational molecular biology.
Robert Tarjan, 194 Nassau St., Room 308
- Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
- Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
- Design and/or analyze various data structures and combinatorial algorithms.
Olga Troyanskaya, Room 320
- Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
- Implement and evaluate one or more gene expression analysis algorithm.
- Develop algorithms for assessment of performance of genomic analysis methods.
- Develop, implement, and evaluate visualization tools for heterogeneous biological data.
David Walker, Room 211
- Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
- Independent Research Topics: Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.
Kevin Wayne, Corwin Hall, Room 040
- Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
- Design and implement computer visualizations of algorithms or data structures.
- Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
- Develop assessment infrastructure and assessments for MOOCs.
Matt Weinberg, 194 Nassau St., Room 222
- Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
- Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc.
- Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?
Opportunities outside the department
We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science. However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.) To do a project with an adviser outside of computer science you must have permission of the department. This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.
Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.
Branko Glisic, Engineering Quadrangle, Room E330
- Documentation of historic structures
- Cyber physical systems for structural health monitoring
- Developing virtual and augmented reality applications for documenting structures
- Applying machine learning techniques to generate 3D models from 2D plans of buildings
- Contact : Rebecca Napolitano, rkn2 (@princeton.edu)
Mihir Kshirsagar, Sherrerd Hall, Room 315
Center for Information Technology Policy.
- Consumer protection
- Content regulation
- Competition law
- Economic development
- Surveillance and discrimination
Sharad Malik, Engineering Quadrangle, Room B224
Select a Senior Thesis Adviser for the 2020-21 Academic Year.
- Design of reliable hardware systems
- Verifying complex software and hardware systems
Prateek Mittal, Engineering Quadrangle, Room B236
- Internet security and privacy
- Social Networks
- Privacy technologies, anonymous communication
- Network Science
- Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
- Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
- Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.
Ken Norman, Psychology Dept, PNI 137
- Research Areas: Memory, the brain and computation
- Lab: Princeton Computational Memory Lab
Potential research topics
- Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG)
- Neural network simulations of learning and memory
Caroline Savage
Office of Sustainability, Phone:(609)258-7513, Email: cs35 (@princeton.edu)
The Campus as Lab program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its website .
An example from Computer Science could include using TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a live energy heatmap of campus .
Other potential projects include:
- Apply game theory to sustainability challenges
- Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
- How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?
Janet Vertesi, Sociology Dept, Wallace Hall, Room 122
- Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
- Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.
David Wentzlaff, Engineering Quadrangle, Room 228
Computing, Operating Systems, Sustainable Computing.
- Instrument Princeton's Green (HPCRC) data center
- Investigate power utilization on an processor core implemented in an FPGA
- Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
- Other topics in parallel computer architecture or operating systems
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Natural Language Processing (NLP) Projects & Topics For Beginners [2023]
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology…
Table of Contents
NLP Projects & Topics
Natural Language Processing or NLP is an AI component concerned with the interaction between human language and computers. When you are a beginner in the field of software development, it can be tricky to find NLP projects that match your learning needs. So, we have collated some examples to get you started. So, if you are a ML beginner, the best thing you can do is work on some NLP projects.
We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting NLP projects which beginners can work on to put their knowledge to test. In this article, you will find top NLP project ideas for beginners to get hands-on experience on NLP.
But first, let’s address the more pertinent question that must be lurking in your mind: why to build NLP projects ?
When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience.
NLP is all about analyzing and representing human language computationally. It equips computers to respond using context clues just like a human would. Some everyday applications of NLP around us include spell check, autocomplete, spam filters, voice text messaging, and virtual assistants like Alexa, Siri, etc. As you start working on NLP projects , you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.
In the last few years, NLP has garnered considerable attention across industries. And the rise of technologies like text and speech recognition, sentiment analysis, and machine-to-human communications, has inspired several innovations. Research suggests that the global NLP market will hit US$ 28.6 billion in market value in 2026.
When it comes to building real-life applications, knowledge of machine learning basics is crucial. However, it is not essential to have an intensive background in mathematics or theoretical computer science. With a project-based approach, you can develop and train your models even without technical credentials. Learn more about NLP Applications.
To help you in this journey, we have compiled a list of NLP project ideas , which are inspired by actual software products sold by companies. You can use these resources to brush up your ML fundamentals, understand their applications, and pick up new skills during the implementation stage. The more you experiment with different NLP projects , the more knowledge you gain.
Before we dive into our lineup of NLP projects , let us first note the explanatory structure.
The project implementation plan
All the projects included in this article will have a similar architecture, which is given below:
- Implementing a pre-trained model
- Deploying the model as an API
- Connecting the API to your main application
This pattern is known as real-time inference and brings in multiple benefits to your NLP design. Firstly, it offloads your main application to a server that is built explicitly for ML models. So, it makes the computation process less cumbersome. Next, it lets you incorporate predictions via an API. And finally, it enables you to deploy the APIs and automate the entire infrastructure by using open-source tools, such as Cortex.
Here is a summary of how you can deploy machine learning models with Cortex:
- Write a Python script to serve up predictions.
- Write a configuration file to define your deployment.
- Run ‘cortex deploys’ from your command line.
Now that we have given you the outline let us move on to our list!
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So, here are a few NLP Projects which beginners can work on:
NLP Project Ideas
This list of NLP projects for students is suited for beginners, intermediates & experts. These NLP projects will get you going with all the practicalities you need to succeed in your career.
Further, if you’re looking for NLP projects for final year , this list should get you going. So, without further ado, let’s jump straight into some NLP projects that will strengthen your base and allow you to climb up the ladder. This list is also great for Natural Language Processing projects in Python .
Here are some NLP project idea that should help you take a step forward in the right direction.
1. A customer support bot
One of the best ideas to start experimenting you hands-on NLP projects for students is working on customer support bot. A conventional chatbot answers basic customer queries and routine requests with canned responses. But these bots cannot recognize more nuanced questions. So, support bots are now equipped with artificial intelligence and machine learning technologies to overcome these limitations. In addition to understanding and comparing user inputs, they can generate answers to questions on their own without pre-written responses.
For example, Reply.ai has built a custom ML-powered bot to provide customer support. According to the company, an average organization can take care of almost 40 % of its inbound support requests with their tool. Now, let us describe the model required to implement a project inspired by this product.
You can use Microsoft’s DialoGPT, which is a pre-trained dialogue response generation model. It extends the systems of PyTorch Transformers (from Hugging Face) and GPT-2 (from OpenAI) to return answers to the text queries entered. You can run an entire DialoGPT deployment with Cortex. There are several repositories available online for you to clone. Once you have deployed the API, connect it to your front-end UI, and enhance your customer service efficiency!
Read: How to make chatbot in Python?
2. A language identifier
Have you noticed that Google Chrome can detect which language in which a web page is written? It can do so by using a language identifier based on a neural network model.
This is an excellent NLP projects for beginners. The process of determining the language of a particular body of text involves rummaging through different dialects, slangs, common words between different languages, and the use of multiple languages in one page. But with machine learning, this task becomes a lot simpler.
You can construct your own language identifier with the fastText model by Facebook. The model is an extension of the word2vec tool and uses word embeddings to understand a language. Here, word vectors allow you to map a word based on its semantics — for instance, upon subtracting the vector for “male” from the vector for “king” and adding the vector for “female,” you will end up with the vector for “queen.”
A distinctive characteristic of fastText is that it can understand obscure words by breaking them down into n-grams. When it is given an unfamiliar word, it analyzes the smaller n-grams, or the familiar roots present within it to find the meaning. Deploying fastTExt as an API is quite straightforward, especially when you can take help from online repositories.
3. An ML-powered autocomplete feature
Autocomplete typically functions via the key value lookup, wherein the incomplete terms entered by the user are compared to a dictionary to suggest possible options of words. This feature can be taken up a notch with machine learning by predicting the next words or phrases in your message.
Here, the model will be trained on user inputs instead of referencing a static dictionary. A prime example of an ML-based autocomplete is Gmail’s ‘Smart Reply’ option, which generates relevant replies to your emails. Now, let us see how you can build such a feature.
For this project, you can use the RoBERTa language model. It was introduced at Facebook by improving Google’s BERT technique. Its training methodology and computing power outperform other models in many NLP metrics.
To receive your prediction using this model, you would first need to load a pre-trained RoBERTa through PyTorch Hub. Then, use the built-in method of fill_mask(), which would let you pass in a string and guide your direction to where RoBERTa would predict the next word or phrase. After this, you can deploy RoBERTa as an API and write a front-end function to query your model with user input. Mentioning NLP projects can help your resume look much more interesting than others.
4. A predictive text generator
This is one of the interesting NLP projects. Have you ever heard of the game AI Dungeon 2? It is a classic example of a text adventure game built using the GPT-2 prediction model. The game is trained on an archive of interactive fiction and demonstrates the wonders of auto-generated text by coming up with open-ended storylines. Although machine learning in the area of game development is still at a nascent stage, it is set to transform experiences in the near future. Learn how python performs in game development .
DeepTabNine serves as another example of auto-generated text. It is an ML-powered coding autocomplete for a variety of programming languages. You can install it as an add-on to use within your IDE and benefit from fast and accurate code suggestions. Let us see how you can create your own version of this NLP tool.
You should go for Open AI’s GPT-2 model for this project. It is particularly easy to implement a full pre-trained model and to interact with it thereafter. You can refer to online tutorials to deploy it using the Cortex platform. And this is the perfect idea for your next NLP project!
Read: Machine Learning Project Ideas
5. A media monitor
One of the best ideas to start experimenting you hands-on NLP projects for students is working on media monitor. In the modern business environment, user opinion is a crucial denominator of your brand’s success. Customers can openly share how they feel about your products on social media and other digital platforms. Therefore, today’s businesses want to track online mentions of their brand. The most significant fillip to these monitoring efforts has come from the use of machine learning.
For example, the analytics platform Keyhole can filter all the posts in your social media stream and provide you with a sentiment timeline that displays the positive, neutral, or negative opinion. Similarly, an ML-backed sift through news sites. Take the case of the financial sector where organizations can apply NLP to gauge the sentiment about their company from digital news sources.
Such media analytics can also improve customer service. For example, providers of financial services can monitor and gain insights from relevant news events (such as oil spills) to assist clients who have holdings in that industry.
You can follow these steps to execute a project on this topic:
- Use the SequenceTagger framework from the Flair library. (Flair is an open-source repository built on PyTorch that excels in dealing with Named Entity Recognition problems.)
- Use Cortex’s Predictor API to implement Flair.
We are currently experiencing an exponential increase in data from the internet, personal devices, and social media. And with the rising business need for harnessing value from this largely unstructured data, the use of NLP instruments will dominate the industry in the coming years.
Such developments will also jumpstart the momentum for innovations and breakthroughs, which will impact not only the big players but also influence small businesses to introduce workarounds.
Also read: AI Project Ideas and Topics for Beginners
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Natural language processing techniques to use in python.
Making computers read unorganized texts and extract useful information from them is the aim of natural language processing (NLP). Many NLP approaches can be implemented using a few lines of Python code, courtesy of accessible libraries like NLTK, and spaCy. These approaches can also work great as NLP topics for presentation .
Here are some techniques of Natural Language Processing projects in Python –
- Named Entity Recognition or NER – A technique called named entity recognition is used to find and categorise named entities in text into groups like people, organisations, places, expressions of times, amounts, percentages, etc. It is used to improve content classification, customer service, recommendation systems, and search engine algorithms, among other things.
- Analysis of Sentiment – One of the most well-known NLP approaches, sentiment analysis examines text (such as comments, reviews, or documents) to identify whether the information is good, poor, or indifferent. Numerous industries, including banking, healthcare, and customer service, can use it.
- BoW or Bag of Words – A format that transforms text into stationary variables is called the Bag of Words (BoW) model. This makes it easier for us to convert text to numbers to be used in machine learning. The model is simply interested in the number of terms in the text and isn’t focused on word order. It may be used for document categorisation, information retrieval, and NLP. Cleaning raw text, tokenisation, constructing a vocabulary, and creating vectors are all steps in the normal BoW approach.
- TF-IDF (Term Frequency – Inverse Document Frequency) – The TF-IDF calculates “weights” that describe how significant a word is in the document. The quantity of documents that include a term reduces the TF-IDF value, which rises according to the frequency of its use in the document. Simply said, the phrase is rare, more distinctive, or more important the higher the TF-IDF score, and vice versa. It has uses in information retrieval, similar to how browsers try to yield results that are most pertinent to your request.
TF and IDF are calculated in different ways.
TF = (Number of duplicate words in a document) / (Number of words in a document)
IDF = Log {(Number of documents) / (Number of documents with the word)}
- Wordcloud – A common method for locating keywords in a document is word clouds. In a Wordcloud, words that are used more frequently have larger, stronger fonts, while those that are used less frequently have smaller, thinner fonts. With the ‘Wordcloud’ library and the ‘stylecloud’ module, you can create simplistic Wordclouds in Python. This makes NLP projects in Python very successful.
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Nlp research topics – .
To ace NLP projects in Python , it is necessary to conduct thorough research. Here are some NLP research topics that will help you in your thesis and also work great as NLP topics for presentation –
- Biomedical Text Mining
- Computer Vision and also NLP
- Deep Linguistic Processing
- Controlled Natural Language
- Language Resources and also Architectures for NLP
- Sentiment Analysis and also Opinion Mining
- NLP includes Artificial Intelligence
- Issues includes Natural language understanding and also Creation
- Extraction of Actionable Intelligence also from Social Media
- Efficient Information also Extraction Techniques
- Use of Rule also based Approach or Statistical Approach
- Topic Modelling in Web data
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In this article, we covered some NLP projects that will help you implement ML models with rudimentary knowledge software development. We also discussed the real-world applicability and functionality of these products. So, use these topics as reference points to hone your practical skills and propel your career and business forward!
Only by working with tools and practise can you understand how infrastructures work in reality. Now go ahead and put to test all the knowledge that you’ve gathered through our NLP projects guide to build your very own NLP projects!
If you wish to improve your NLP skills, you need to get your hands on these NLP projects. If you’re interested to learn more about machine learning online course , check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
How easy it is to implement these projects?
These projects are very basic, someone with a good knowledge of NLP can easily manage to pick and finish any of these projects.
Can I do this projects on ML Internship?
Yes, as mentioned, these project ideas are basically for Students or Beginners. There is a high possibility that you get to work on any of these project ideas during your internship.
Why do we need to build NLP projects?
What is natural language processing.
Natural language processing (NLP) is a subject of computer science—specifically, a branch of artificial intelligence (AI)—concerning the ability of computers to comprehend text and spoken words in the same manner that humans can. Computational linguistics—rule-based human language modeling—is combined with statistical, learning algorithms, and deep learning models.
How to implement any NLP project?
The design of all the projects will be the same: Implementing a pre-trained model, deploying the model as an API, and connecting the API to your primary application. Real-time inference is a pattern that delivers several benefits to your NLP design. To begin with, it offloads your core application to a server designed specifically for machine learning models. As a result, the computation procedure is simplified. Then, using an API, you may incorporate predictions. Finally, it allows you to use open-source tools like Cortex to install APIs and automate the entire architecture.
How to construct a language identifier?
This is a fantastic NLP project for newcomers. The method of identifying the language of a body of text entails combing through many dialects, slangs, cross-language common terms, and the use of numerous languages on a single page. This task, however, becomes a lot easier with machine learning. With Facebook's fastText concept, you can create your own language identifier. The model employs word embeddings to comprehend a language and is an expansion of the word2vec tool. Word vectors enable you to map a word based on its semantics — for example, you can get the vector for Queen by subtracting the vector for Male from the vector for King and adding the vector for Female.
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The Power of Natural Language Processing
- Ross Gruetzemacher
How companies can use NLP to help with brainstorming, summarizing, and researching.
The conventional wisdom around AI has been that while computers have the edge over humans when it comes to data-driven decision making, it can’t compete on qualitative tasks. That, however, is changing. Natural language processing (NLP) tools have advanced rapidly and can help with writing, coding, and discipline-specific reasoning. Companies that want to make use of this new tech should focus on the following: 1) Identify text data assets and determine how the latest techniques can be leveraged to add value for your firm, 2) understand how you might leverage AI-based language technologies to make better decisions or reorganize your skilled labor, 3) begin incorporating new language-based AI tools for a variety of tasks to better understand their capabilities, and 4) don’t underestimate the transformative potential of AI.
Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks , it was still inferior to humans for cognitive and creative ones . But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do.
The most visible advances have been in what’s called “natural language processing” (NLP), the branch of AI focused on how computers can process language like humans do. It has been used to write an article for The Guardian , and AI-authored blog posts have gone viral — feats that weren’t possible a few years ago . AI even excels at cognitive tasks like programming where it is able to generate programs for simple video games from human instructions .
Yet while these stunts may be attention grabbing, are they really indicative of what this tech can do for businesses?
What NLP Can Do
The best known natural language processing tool is GPT-3 , from OpenAI, which uses AI and statistics to predict the next word in a sentence based on the preceding words. NLP practitioners call tools like this “language models,” and they can be used for simple analytics tasks, such as classifying documents and analyzing the sentiment in blocks of text, as well as more advanced tasks, such as answering questions and summarizing reports. Language models are already reshaping traditional text analytics , but GPT-3 was an especially pivotal language model because, at 10x larger than any previous model upon release, it was the first large language model , which enabled it to perform even more advanced tasks like programming and solving high school–level math problems . The latest version, called InstructGPT , has been fine-tuned by humans to generate responses that are much better aligned with human values and user intentions, and Google’s latest model shows further impressive breakthroughs on language and reasoning .
For businesses, the three areas where GPT-3 has appeared most promising are writing, coding, and discipline-specific reasoning. OpenAI, the Microsoft-funded creator of GPT-3, has developed a GPT-3-based language model intended to act as an assistant for programmers by generating code from natural language input. This tool, Codex, is already powering products like Copilot for Microsoft’s subsidiary GitHub and is capable of creating a basic video game simply by typing instructions . This transformative capability was already expected to change the nature of how programmers do their jobs , but models continue to improve — the latest from Google’s DeepMind AI lab, for example, demonstrates the critical thinking and logic skills necessary to outperform most humans in programming competitions.
Models like GPT-3 are considered to be foundation models — an emerging AI research area — which also work for other types of data such as images and video. Foundation models can even be trained on multiple forms of data at the same time, like OpenAI’s DALL·E 2 , which is trained on language and images to generate high-resolution renderings of imaginary scenes or objects simply from text prompts. Due to their potential to transform the nature of cognitive work, economists expect that foundation models may affect every part of the economy and could lead to increases in economic growth similar to the industrial revolution.
A Language-Based AI Research Assistant
In my own work, I’ve been looking at how GPT-3-based tools can assist researchers in the research process. I am currently working with Ought , a San Francisco company developing an open-ended reasoning tool (called Elicit ) that is intended to help researchers answer questions in minutes or hours instead of weeks or months. Elicit is designed for a growing number of specific tasks relevant to research, like summarization, data labeling, rephrasing, brainstorming, and literature reviews.
I’ve found — not surprisingly — that Elicit works better for some tasks than others. Tasks like data labeling and summarization are still rough around the edges, with noisy results and spotty accuracy, but research from Ought and research from OpenAI shows promise for the future.
For example, the rephrase task is useful for writing, but the lack of integration with word processing apps renders it impractical for now. Brainstorming tasks are great for generating ideas or identifying overlooked topics, and despite the noisy results and barriers to adoption, they are currently valuable for a variety of situations. Yet, of all the tasks Elicit offers, I find the literature review the most useful. Because Elicit is an AI research assistant, this is sort of its bread-and-butter, and when I need to start digging into a new research topic, it has become my go-to resource.
All of this is changing how I work. I spend much less time trying to find existing content relevant to my research questions because its results are more applicable than other, more traditional interfaces for academic search like Google Scholar. I am also beginning to integrate brainstorming tasks into my work as well, and my experience with these tools has inspired my latest research, which seeks to utilize foundation models for supporting strategic planning.
How Can Organizations Prepare for the Future?
Identify your text data assets and determine how the latest techniques can be leveraged to add value for your firm..
You are certainly aware of the value of data , but you still may be overlooking some essential data assets if you are not utilizing text analytics and NLP throughout your organization. Text data is certainly valuable for customer experience management and understanding the voice of the customer , but think about other text data assets in your organization: emails, analysts’ reports, contracts, press releases, archives — even meetings and phone calls can be transcribed.
There is so much text data, and you don’t need advanced models like GPT-3 to extract its value. Hugging Face , an NLP startup, recently released AutoNLP , a new tool that automates training models for standard text analytics tasks by simply uploading your data to the platform. The data still needs labels, but far fewer than in other applications. Because many firms have made ambitious bets on AI only to struggle to drive value into the core business, remain cautious to not be overzealous. This can be a good first step that your existing machine learning engineers — or even talented data scientists — can manage.
To take the next step, again, identify your data assets. Many sectors, and even divisions within your organization, use highly specialized vocabularies. Through a combination of your data assets and open datasets, train a model for the needs of specific sectors or divisions. Think of finance. You do not want a model specialized in finance. You want a model customized for commercial banking, or for capital markets. And data is critical, but now it is unlabeled data, and the more the better. Specialized models like this can unlock untold value for your firm.
Understand how you might leverage AI-based language technologies to make better decisions or reorganize your skilled labor.
Language-based AI won’t replace jobs, but it will automate many tasks, even for decision makers. Startups like Verneek are creating Elicit-like tools to enable everyone to make data-informed decisions. These new tools will transcend traditional business intelligence and will transform the nature of many roles in organizations — programmers are just the beginning.
You need to start understanding how these technologies can be used to reorganize your skilled labor. The next generation of tools like OpenAI’s Codex will lead to more productive programmers, which likely means fewer dedicated programmers and more employees with modest programming skills using them for an increasing number of more complex tasks. This may not be true for all software developers, but it has significant implications for tasks like data processing and web development.
Begin incorporating new language-based AI tools for a variety of tasks to better understand their capabilities.
Right now tools like Elicit are just emerging, but they can already be useful in surprising ways. In fact, the previous suggestion was inspired by one of Elicit’s brainstorming tasks conditioned on my other three suggestions. The original suggestion itself wasn’t perfect, but it reminded me of some critical topics that I had overlooked, and I revised the article accordingly. In organizations, tasks like this can assist strategic thinking or scenario-planning exercises. Although there is tremendous potential for such applications, right now the results are still relatively crude, but they can already add value in their current state.
The bottom line is that you need to encourage broad adoption of language-based AI tools throughout your business. It is difficult to anticipate just how these tools might be used at different levels of your organization, but the best way to get an understanding of this tech may be for you and other leaders in your firm to adopt it yourselves. Don’t bet the boat on it because some of the tech may not work out, but if your team gains a better understanding of what is possible, then you will be ahead of the competition. Remember that while current AI might not be poised to replace managers, managers who understand AI are poised to replace managers who don’t.
Do not underestimate the transformative potential of AI.
Large foundation models like GPT-3 exhibit abilities to generalize to a large number of tasks without any task-specific training. The recent progress in this tech is a significant step toward human-level generalization and general artificial intelligence that are the ultimate goals of many AI researchers, including those at OpenAI and Google’s DeepMind. Such systems have tremendous disruptive potential that could lead to AI-driven explosive economic growth, which would radically transform business and society . While you may still be skeptical of radically transformative AI like artificial general intelligence, it is prudent for organizations’ leaders to be cognizant of early signs of progress due to its tremendous disruptive potential.
Consider that former Google chief Eric Schmidt expects general artificial intelligence in 10–20 years and that the UK recently took an official position on risks from artificial general intelligence . Had organizations paid attention to Anthony Fauci’s 2017 warning on the importance of pandemic preparedness, the most severe effects of the pandemic and ensuing supply chain crisis may have been avoided. Ignoring the transformative potential of AI also carries risks, and similar to the supply chain crisis, firms’ inaction or irresponsible use of AI could have widespread and damaging effects on society (e.g., increasing inequality or domain-specific risks from automation). However, unlike the supply chain crisis, societal changes from transformative AI will likely be irreversible and could even continue to accelerate. Organizations should begin preparing now not only to capitalize on transformative AI, but to do their part to avoid undesirable futures and ensure that advanced AI is used to equitably benefit society.
Language-Based AI Tools Are Here to Stay
Powerful generalizable language-based AI tools like Elicit are here, and they are just the tip of the iceberg; multimodal foundation model-based tools are poised to transform business in ways that are still difficult to predict. To begin preparing now, start understanding your text data assets and the variety of cognitive tasks involved in different roles in your organization. Aggressively adopt new language-based AI technologies; some will work well and others will not, but your employees will be quicker to adjust when you move on to the next. And don’t forget to adopt these technologies yourself — this is the best way for you to start to understand their future roles in your organization.
- RG Ross Gruetzemacher is an Assistant Professor of Business Analytics at the W. Frank Barton School of Business at Wichita State University. He is a consultant on AI strategy for organizations in the Bay Area and internationally, and he also works as a Senior Game Master on Intelligence Rising , a strategic role-play game for exploring AI futures.
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Natural Language Processing
Innovations that will enable more natural interaction between human and computers.
Researchers in the Natural Language Processing group work at the intersection of computer science, artificial intelligence, and computational linguistics. Projects in this area aim to understand how human language is used to communicate ideas, and to develop technology for machine analysis, translation, and transformation of multilingual speech and text.
These CS researchers work closely with related researchers in other departments, via the Whiting School’s Center for Language and Speech Processing (CLSP) .
Research Centers & Groups
Center for language and speech processing.
CLSP conducts research across a broad spectrum of fundamental and applied topics including acoustic processing, automatic speech recognition, big data, cognitive modeling, computational linguistics, information extraction, machine learning, machine translation, and text analysis.
Human Language Technology Center of Excellence
HLTCOE focuses on advanced technology for automatically analyzing a wide range of speech, text, and document data in multiple languages.
Linguistics
At JHU, linguistics research focuses on integrating Formal Linguistics within a broader cognitive science perspective by addressing questions about the nature of linguistic representations themselves, their processing, the architecture and learnability of the grammar, the implementation of linguistic theories in terms of neural computations, and language acquisition in the broader context of cognitive development.
Mark Dredze
Jason Eisner
Philipp Koehn
Benjamin van Durme
David Yarowsky
Alan yuille.
Social media fules spread of COVID-19 information - and misinformation
Computer scientist Mark Dredze investigates how social media can help combat—or contribute to—the spread of misinformation during the COVID-19 pandemic.
- Research Topics Ideas of Natural language processing
List of Research Topics Ideas for Natural language processing.
- Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse
- A Taxonomy for Deep Learning in Natural Language Processing
- Prediction of severe chest injury using natural language processing from the electronic health record
- Natural language processing in artificial intelligence
- UMLS-based data augmentation for natural language processing of clinical research literature
- LexNLP: Natural language processing and information extraction for legal and regulatory texts
- Identifying Causality and Contributory Factors of Pipeline incidents by Employing Natural Language Processing and Text Mining Techniques
- Analysis of stroke detection during the COVID-19 pandemic using natural language processing of radiology reports
- Development of a predictive model for retention in HIV care using natural language processing of clinical notes
- Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling
- State of art for semantic analysis of natural language processing
- A deep database of medical abbreviations and acronyms for natural language processing
- Applied natural language processing in mental health big data
- TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing
- Smart Project Management: Interactive Platform Using Natural Language Processing Technology
- Dissociable electrophysiological measures of natural language processing reveal differences in speech comprehension strategy in healthy ageing
- Identifying professions & occupations in health-related social media using natural language processing and evaluation of a challenge data set and solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing …
- Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
- Med7: a transferable clinical natural language processing model for electronic health records
- Text: an R-package for analyzing and visualizing human language using natural language processing and deep learning
- Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure
- Acquiring a semantic lexicon for natural language processing
- Customer satisfaction and natural language processing
- BNLP: Natural language processing toolkit for Bengali language
- Using natural language processing to predict item response times and improve test construction
- Automated detection of periprosthetic joint infections and data elements using natural language processing
- Reliability Testing for Natural Language Processing Systems
- Exploring natural language processing for single-word and multi-word lexical complexity from a second language learner perspective
- Extracting Domain Knowledge Elements of Construction Safety Management: Rule-Based Approach Using Chinese Natural Language Processing
- The market for heritage: Evidence from eBay using natural language processing
- Systematic review of the concept ‘male involvement in maternal health’by natural language processing and descriptive analysis
- Deep Learning on Graphs for Natural Language Processing
- Automated Construction Specification Review with Named Entity Recognition Using Natural Language Processing
- Digital shop floor management enhanced by natural language processing
- Natural language processing of radiology reports for the identification of patients with fracture
- Development of a Natural Language Processing based Deep Learning Model for Automated HS Code Classification of the Imported Goods
- Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives
- Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients …
- A Crash Course on Ethics for Natural Language Processing
- Facilitating the Compliance of Process Models with Critical System Engineering Standards using Natural Language Processing.
- Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing
- Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record …
- Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing
- Identifying botnet IP address clusters using natural language processing techniques on honeypot command logs
- Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement …
- Models of lexical semantics in the algorithms for natural language processing
- … IDENTIFYING EROSIVE DISEASE FROM RADIOLOGY REPORTS OF VETERANS WITH INFLAMMATORY ARTHRITIS USING NATURAL LANGUAGE PROCESSING
- Applying natural language processing algorithm for predicting consumer product preferences in retail stores
- Classifying Fake News Articles using Natural Language Processing and Supervised Learning Estimator
- Natural Language Processing
- A Natural Language Processing Approach to Mine Online Reviews Using Topic Modelling
- Text Analytics and Natural Language Processing
- Product Recommendation Platform Based on Natural Language Processing
- Comparative Question Answering System based on Natural Language Processing and Machine Learning
- TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
- Implementation of Natural Language Processing in Seller-bot for SMEs
- Machine learning applied in natural language processing
- Identification of user stories in software issues records applying pre-trained natural language processing models.
- Natural language processing (NLP) and machine learning (ML) model for predicting CMS OP-35 categories among patients receiving chemotherapy.
- Bankruptcy Prediction Model Based on Business Risk Reports: Use of Natural Language Processing Techniques
- Domain Experts and Natural language Processing in the Evaluation of Circular Economy Business Model Ontology
- Non-Functional Requirement Detection Using Machine Learning and Natural Language Processing
- … STATIN NONADHERENCE IN A DIVERSE, REAL-WORLD POPULATION USING ELECTRONIC HEALTH RECORDS AND NATURAL LANGUAGE PROCESSING
- Research on classification method of answering questions in network classroom based on natural language processing technology
- Introduction to Natural Language Processing
- The Application of Thai Natural Language Processing
- RuThes Thesaurus for Natural Language Processing
- The Utilization of Natural Language Processing in Predicting Emergency Department Overcrowding: A Literature Review
- Thai Natural Language Processing Programming
- Neural Networks for Natural Language Processing
- Streamlining the Identification of Emerging Tasks in the O* NET System Using Natural Language Processing (NLP): Technical Summary
- [BOOK][B] Natural Language Processing: A Machine Learning Perspective
- An Unsupervised Content-Based Article Recommendation System Using Natural Language Processing
- Biomedical Corpora and Natural Language Processing on Clinical Text in Languages Other Than English: A Systematic Review
- Text Summarization Using Natural Language Processing
- A Novel Natural Language Processing Approach to Mine Skills from Job Vacancies and Courses Syllabus: the Case Study of Toscana Open Research Regional …
- The portability of natural language processing methods to detect suicidality from unstructured clinical text in US and UK electronic health records
- Review on Usage of Hidden Markov Model in Natural Language Processing
- Natural language processing bots
- Abstract PO-050: Identifying de novo stage IV breast cancer (DNIV) cases in Electronic Health Records (EHR) using natural language processing
- 17 Using Self-Organizing Maps for Natural Language Processing
- Heuristic Approach Towards COVID-19: Big Data Analytics and Classification with Natural Language Processing
- Natural language understanding and multimodal discourse analysis for interpreting extremist communications and the re-use of these materials online
- Reading functional requirements using machine learning-based language processing
- Language model is all you need: Natural language understanding as question answering
- BnVec: Towards the Development of Word Embedding for Bangla Language Processing
- Natural Language Interfaces to Databases: A Survey on Recent Advances
- Spoken Language Dialogue Systems
- Learning transferable visual models from natural language supervision
- Interaction-Integrated Network for Natural Language Moment Localization
- Generating Relations from Natural Language Using REXTOR
- Generate natural language explanations for recommendation
- Improving Natural Language Queries Search and Retrieval through Semantic Image Annotation Understanding
- Self-Contextualized Attention for Abusive Language Identification
- Predicting Polymer’s Glass Transition Temperature by A Chemical Language Processing Model
- Building natural language responses from natural language questions in the spatio-temporal context
- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big???
- Overview of the wanlp 2021 shared task on sarcasm and sentiment detection in arabic
- Natural Language Understanding
- Querying knowledge graphs in natural language
- BanglaBERT: Combating Embedding Barrier for Low-Resource Language Understanding
- Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining
- Sarcasmdet at sarcasm detection task 2021 in arabic using arabert pretrained model
- Misspelling Correction with Pre-trained Contextual Language Model
- The IDC System for Sentiment Classification and Sarcasm Detection in Arabic
- What does BERT learn from Arabic machine reading comprehension datasets?
- AI-and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text
- Word sense disambiguation in Tamil using Indo-WordNet and cross-language semantic similarity
- Cortical processing of reference in language revealed by computational models
- Dissecting Computer Vision: Using Natural Language Processing to Analyze Scientific Content
- Part-of-Speech Tagging in Mizo Language: A Preliminary Study
- Automating test oracles from restricted natural language agile requirements
- Listening with great expectations: A study of predictive natural speech processing
- Deep Learning for the Russian Language
- SUPERB: Speech processing Universal PERformance Benchmark
- Natural Language Processing in Pidgin Language
- The language of a virus
- There’s no English word for that: design, translation, and natural language processing
- DeepBlueAI at WANLP-EACL2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in Arabic
- Arcov-19: The first arabic covid-19 twitter dataset with propagation networks
- Classification of Noisy Free-Text Prostate Cancer Pathology Reports Using Natural Language Processing
- Multilingual evaluation of pre-processing for BERT-based sentiment analysis of tweets
- Language Detection Engine for Multilingual Texting on Mobile Devices
- Specifies of construction industry terminology and methods of analysis of the natural language sentences similarity by using isomorphism of their structures
- Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
- [email protected] DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian Languages
- Adult content detection on arabic twitter: Analysis and experiments
- An effective contextual language modeling framework for speech summarization with augmented features
- A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification
- EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain
- Core Language of Thought
- SPEECH PROCESSING Electronic textbook
- Comparing pre-trained language models for Spanish hate speech detection
- Variational model for low-resource natural language generation in spoken dialogue systems
- Ask2Transformers: Zero-Shot Domain labelling with Pre-trained Language Models
- The finsim 2020 shared task: Learning semantic representations for the financial domain
- NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization
- Findings of the shared task on Hope Speech Detection for Equality, Diversity, and Inclusion
- Introducing A large Tunisian Arabizi Dialectal Dataset for Sentiment Analysis
- Understanding the Language of ISIS: An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning
- Hierarchical syntactic processing is beyond mere associating: Functional magnetic resonance imaging evidence from a novel artificial grammar
- Research on Text Classification Method Based on LSTM Neural Network Model
- Discrete signal processing on meet/join lattices
- Validating, verifying and testing timed data-flow reactive systems in Coq from controlled natural-language requirements
- Assessing Emoji Use in Modern Text Processing Tools
- Types of Data
- Roles for event representations in sensorimotor experience, memory formation, and language processing
- Private Text Classification with Convolutional Neural Networks
- Semantic Analysis
- Ontology Learning for Systems Engineering Body of Knowledge
- Location Extraction to Inform a Spanish-Speaking Community About Traffic Incidents
- Dynamical Cognitive Modeling of Syntactic Processing and Eye Movement Control in Reading
- Method for Intelligent Information Processing Using Software Reuse Library and Blockchain Architecture
- Analysing and Plotting Online Customer Emotions Using a Lexicon-Based Approach
- Constrained BERT BiLSTM CRF for understanding multi-sentence entity-seeking questions
- FinSBD-2020: The 2nd Shared Task on Sentence Boundary Detection in Unstructured Text in the Financial Domain
- Natural Language Grammar Induction with a Generative Constituent-Context Model ½
- Text-based Identification of Herpes Zoster Ophthalmicus with Ocular Involvement in the Electronic Health Record: A Population-based Study
- A Synonym-Based Bi-LSTM Model for Machine Reading Comprehension
- A method based on multi-standard active learning to recognize entities in electronic medical record [J]
- Thai Word Segmentation
- Leveraging Multilingual Transformers for Hate Speech Detection
- CityFlow-NL: Tracking and Retrieval of Vehicles at City Scaleby Natural Language Descriptions
- Question Answering System Using LSTM and Keyword Generation
- [email protected] LaySumm 20, LongSumm 20
- Question answering
- An Approach for Morphological Analyzer Rules for Dravidian Telugu Language
- An ATC instruction processing-based trajectory prediction algorithm designing
- Automatic Feedback Generation for Dialog-Based Language Tutors Using Transformer Models and Active Learning
- Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
- REDASA: A Secure Continually Updating Web-Source Processing Pipeline supporting a REaltime DAta Synthesis and Analysis of Scientific Literature
- A Gentle Introduction to ML and NLP
- Relationship identification between conversational agents using emotion analysis
- Semi-Supervised Language Models for Identification of Personal Health Experiential from Twitter Data: A Case for Medication Effects
- Substructure Substitution: Structured Data Augmentation for NLP
- Understanding Few-Shot Commonsense Knowledge Models
- Sentiment Analysis Model and Its Role in Determining Social Media’s Influence on Decision Making
- Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection
- A Primer on Word Embedding
- Morphological Segmentation for Seneca
- Developing a Shared Task for Speech Processing on Endangered Languages
- A scoping review on the use, processing and fusion of geographic data in virtual assistants
- ASRNN: a recurrent neural network with an attention model for sequence labeling
- Sentiment Analysis of Arabic Documents: Main Challenges and Recent Advances
- Integrated Secured Digitized Home Using IoT-Based Sensors
- Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa–A Large Romanian Sentiment Data Set
- Questions Generation for Reading Comprehension Using Coherence Relations
- A longitudinal neuroimaging dataset on language processing in children ages 5, 7, and 9 years old
- Autonomy Voice Assistant for NPAS (NASA Platform for Autonomous Systems)
- Cross-Document Language Modeling
- … neuronales multilingues pour le traitement automatique des langues naturelles.(Multilingual neural architectures for natural language processing).
- Deep Learning applications for COVID-19
- The preposition project
- Knowledge-Based Verification of Concatenative Programming Patterns Inspired by Natural Language for Resource-Constrained Embedded Devices
- A Survey on Deep Learning-Based Automatic Text Summarization Models
- NLP in Virtual Assistants
- Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology …
- Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
- Analysis, generation and more using genetic algorithms
- Academy and Company Needs: The Past and Future of NLP
- Moses and the Character-Based Random Babbling Baseline: CoAStaL at AmericasNLP 2021 Shared Task
- BRDS: An FPGA-based LSTM Accelerator with Row-Balanced Dual-Ratio Sparsification
- Delivering public services through social media in European local governments. An interpretative framework using semantic algorithms
- Differentiating characteristics of patients with asthma in the severe acute respiratory syndrome coronavirus 2 infection
- Zero-shot Learning by Generating Task-specific Adapters
- Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships
- Structuring electronic dental records through deep learning for a clinical decision support system
- Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
- Question Answering Over Knowledge Base: A Scheme for Integrating Subject and the Identified Relation to Answer Simple Questions
- CapGen: A Neural Image Caption Generator with Speech Synthesis
- A Conceptual Framework for Sensitive Big Data Publishing
- Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping
- Phone Calls Speech-to-Text: A Comparison Between APIs for the Portuguese Language
- Artificial intelligence–enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states …
- Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)
- Gated Convolutional Neural Networks for Text Classification
- Deep Learning for Latent Events Forecasting in Twitter Aided Caching Networks
- Reasons for discontinuing insulin and factors associated with insulin discontinuation in patients with type 2 diabetes mellitus: a real-world evidence study
- Deep learning enabled semantic communication systems
- Lex-BERT: Enhancing BERT based NER with lexicons
- Analysis of the Effect of Topic Modeling on General Corpus Mixed with In-Domain Text for English-Hindi Translation
- Detection of Brain Tumor Using Image Processing and Neural Networks
- Integrating heterogeneous thesauruses for Chinese synonyms
- Assisted Requirements Engineering-What Will Remain in the Hands of the Future Requirements Engineer?(Invited Keynote)
- Authorship identification of a russian-language text using support vector machine and deep neural networks
- Automating the Compilation of Potential Core-Outcomes for Clinical Trials
- Automatic Text Summarization for Marine Natural Products Literature
- Fuzzy Lexicon-Based Approach for Sentiment Analysis of Blog and Microblog Text
- Coreference Resolution without Span Representations
- Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
- Application of Artificial Intelligence for Medical Research. Biomolecules 2021, 11, 90
- Preparing the NooJ German Module for the Analysis of a Learner Spoken Corpus
- A study of Turkish emotion classification with pretrained language models
- [email protected] DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attention
- Experiments on a Guarani Corpus of News and Social Media
- Experimental Evaluation of Deep Learning models for Marathi Text Classification
- Artificial intelligence in pancreaticobiliary endoscopy
- Advances and challenges in conversational recommender systems: A survey
- Comparison of Various Word Embeddings for Hate-Speech Detection
- Future of AI
- Machine translation
- Chinese Spelling Error Detection Using a Fusion Lattice LSTM
- SogouQ: The First Large-Scale Test Collection with Click Streams Used in a Shared-Task Evaluation
- Lexicon-Based Sentiment Analysis
- Leveraging Acoustic and Linguistic Embeddings from Pretrained Speech and Language Models for Intent Classification
- Text Mining for Secure Cyber Space
- Chatbot-Based Training for Project Management: Another Way of Corporate Training or a Must-Have Tool for Sustainable Education?
- Mapping climate discourse to climate opinion: An approach for augmenting surveys with social media to enhance understandings of climate opinion in the …
- Mobile data science and intelligent apps: concepts, ai-based modeling and research directions
- TLS-ART-MC, A New Algorithm for Thai Word Segmentation
- PRCMLA: Product Review Classification Using Machine Learning Algorithms
- Translating Sentimental Statements Using Deep Learning Techniques
- Efficient attention: Attention with linear complexities
- Machine Translation Believability
- ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis
- Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction
- Author profiling and related applications
- University Learning and Anti-Plagiarism Back-End Services
- A Hybrid Intelligent Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet
- Knowledge distillation for improved accuracy in spoken question answering
- Text Classification
- Research on Express Information Extraction Based on Multiple Sequence Labeling Models
- Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers
- Controlling Sparseness in Multinomial Models
- Mixed Depth Representations for Interpreting Questions and Requests
- Machine Learning Approach to Stock Prediction and Analysis
- Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya
- Automatic Sentiment Analysis of Texts: The Case of Russian
- EventPlus: A Temporal Event Understanding Pipeline
- Parasocial cues: The ubiquity of parasocial relationships on Twitch
- User generated content for exploring factors affecting intention to use travel and food delivery services
- Information retrieval: a view from the Chinese IR community
- NormCG: A Novel Deep Learning Model for Medical Entity Linking
- Artificial Intelligence Clinical Evidence Engine for Automatic Identification, Prioritization, and Extraction of Relevant Clinical Oncology Research
- A character representation enhanced on-device Intent Classification
- Convolutional Neural Networks
- Social media rumor refutation effectiveness: Evaluation, modelling and enhancement
- Making sense of lexical acquisition
- NLP in Customer Service
- Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling
- Transformer networks for trajectory forecasting
- Overview of the sixth social media mining for health applications (# smm4h) shared tasks at naacl 2021
- Automatic Word Spacing of Korean Using Syllable and Morpheme
- Learning Better Sentence Representation with Syntax Information
- Sentiment Analysis Using Twitter
- Application of Artificial Intelligence for Medical Research
- Using virtual edges to improve the discriminability of co-occurrence text networks
- A framework for the automatic processing of Basque
- Newspaper Identification in Hindi
- Semantic Composition of Word-Embeddings with Genetic Programming
- Artificial intelligence in dermatopathology: diagnosis, education, and research
- A review of uncertainty quantification in deep learning: Techniques, applications and challenges
- [DOC][DOC] The Impact of Document Collection Characteristics on Information Access in Digital Libraries
- DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networks
- Systematic problem-specification in innovation science using language
- I-BERT: Integer-only BERT Quantization
- They’ll know it when they see it: Analyzing post-release feedback from the Android community
- Moroccan Data-Driven Spelling Normalization Using Character Neural Embedding
- Brave Men and Emotional Women: Analyzing Gender Bias in Bollywood Songs
- Topic Modeling in Russia: Current Approaches and Issues in Methodology
- A deep learning based algorithm for multi-criteria recommender systems
- Spatio-Temporal Self-Attention Weighted VLAD Neural Network for Action Recognition
- Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance
- The More Detail, the Better?–Investigating the Effects of Semantic Ontology Specificity on Vector Semantic Classification with a Plains Cree/nêhiyawêwin Dictionary
- Deep Neural Network Based Relation Extraction: An Overview
- Techniques Behind Smart Home Automation System Using NLP and IoT
- Sarcasm Detection Approaches Survey
- Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database
- Improving Event Detection by Exploiting Label Hierarchy
- Explaining Deep Learning-Based Classification of Textual Tweets
- INTELLIGENT SOCIAL MEDIA ACTIVITY MONITOR
- A survey on contrastive self-supervised learning
- Transfer Learning and Augmentation for Word Sense Disambiguation
- Research on Text Emotion Analysis and Product Performance based on NLP and VAR Model
- Use of RNN in Devangari Script
- Selected papers from EKAW 2018
- Control Focus in Standard Forms: An Assessment through Text Mining and NLP
- TIB’s Visual Analytics Group at MediaEval’20: Detecting Fake News on Corona Virus and 5G Conspiracy
- Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes
- NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
- A Study on Different Functionalities and Performances among Different Activation Functions across Different ANNs for Image Classification
- Variations in Word Usage for the Financial Domain
- A novel word similarity measure method for IoT-enabled Healthcare applications
- Context-aware query design combines knowledge and data for efficient reading and reasoning
- Automatic tuning of hyperparameters using Bayesian optimization
- NLP in Banking, Financial Services, and Insurance (BFSI)
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Natural Language Processing Thesis Topics
Natural Language Processing Thesis Topics is our brand new initiative that serves young scholars also with the Nobel motive of academic enhancement and also support.Thesis Topics brings together a team of world-class experts who will work exclusively also for you in your ideal thesis.Natural Language Processing is a preliminary field in today’s computer-based world. It is also the base of various lingual interactive programs such as native language browsing, and Google language translator NLP can be defined as a medium that enables human language interaction with the computer. And it is also based on an essential research topic as it houses domains such as machine learning.
Natural Language Processing Topics
We are also proud to say that more than 2000+ scholars have benefited from our Natural Language Processing Thesis Topics . Our helping hand leads you from the starting line to the finished line. We also promise you an incredible offer that leads you straight toward victory. And also, we are the professional home of nearly 100+ researchers whose area of expertise is NLP. We also offer you complete guidance right from the pre-training session until viva voce preparation.
…“NLP belongs to the growing field of artificial intelligence and computational linguistic also that plays a great part in creating computers that can understand human language.”
NLP Characteristics
- -It is formed on the basis of computer interface
- -Natural language generation and also natural language understanding are the two important challenges of NLP
- Relationship extraction
- Named entity recognition
- Morphological segmentation
- Automatic summarization
- Speech tagging and parsing
- Conference resolution
- Discourse analysis
- Machine translation
- Query expansion
- Sentiment analysis and also breaking
- Speech recognition and also breaking
- Text simplification
- Speech processing
- Tokenization
- Text summarization
- Word sense disambiguation
- Deep analytics
- Native language identification
- Word segmentation
Multiple Techniques and Algorithms in NLP
- Keyword extraction and also topic modelling uses latent semantic analysis and latent dirichlet allocation
- Maxtent for word alignment also in machine translation
- Edit distance and soundex also for spell checking
- CRF++ and HMM for chunking and also extracting names entity
- Hobbs algorithm, leass algorithm and also cantering theory for anaphora resolution
- Chart pausing algorithm also for pausing
- SVM and Naive Bayes also for document classification
Analysis Methodologies in NLP
- Semantic Grammars
- Case frame instantiation
- For interpretation of large groups of words syntactically driven parsing
- Input utterance also for language interpretation is interpreted by pattern matching
Development Tools and Software
- Festival speech synthesis system
- Regulus Grammar Compiler
- Never ending language learning
- JGibLDA- v1.0
- Apache hucene core
- Standford NLP
NLP Toolkits
- DELPH-IN (CH,LISP)
- Deeplearning4 (Scala and also Java)
- Mallet (Java)
- Natural language toolkit(python)
- Lingua stream(Java)
- Distinguo(C++)
- Modular Audio Recognition Framework(Java)
- Monty Lingua(Python and also Java)
- Gensim(python based)
Research Areas in Natural Language Topics
- Anomaly and also Detection of reuse
- Biomedical text mining
- Computer assisted reviewing
- Computer-human dialogue systems
- Computer vision and also NLP
- Controlled natural language
- Deep linguistic processing
- Efficient Information also extraction techniques
- Events and Semantics of time
- Extraction of actionable intelligence also from social media
- Fact recognition and also spatiotemporal anchoring of events
- Identification and also Text correction
- Issues includes Natural language understanding and also creation
- Language resources and also architectures for NLP
- Machine translation also based issues
- Natural language user interfaces
- NLP includes artificial intelligence
- POST problems and also Computational linguistic
- Sentiment analysis and also opinion mining
- Speech processing also using linguistic rules
- Text processing chain enhancement applying semantic role labelling, co-reference resolution and also spatial expressions recognition
- Topic modelling in Web data
- Use of rule also based approach or statistical approach
Recent Applications of NLP
- -Dialogue also based applications
- -Text based applications
- Database access
- Plagiarism detection
- Natural language search
- Information retrieval
- Grammar and spelling checkers
- Spoken language control systems
- Language translation
- Speaker Verification and also Identification
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- Innovative 12+ Natural Language Processing Thesis Topics
Generally, natural language processing is the sub-branch of Artificial Intelligence (AI). Natural language processing is otherwise known as NLP. It is compatible in dealing with multi-linguistic aspects and they convert the text into binary formats in which computers can understand it. Primarily, the device understands the texts and then translates according to the questions asked. These processes are getting done with the help of several techniques. As this article is concentrated on delivering the natural language processing thesis topics , we are going to reveal each and every aspect that is needed for an effective NLP thesis .
NLP has a wide range of areas to explore in which enormous researches will be conducted. As the matter of fact, they analyses emotions, processes images, summarize texts, answer the questions & translates automatically, and so on.
Thesis writing is one of the important steps in researches. As they can deliver the exact perceptions of the researcher to the opponents hence it is advisable to frame the proper one. Let us begin this article with an overview of the NLP system . Are you ready to sail with us? Come on, guys!!!
“This is the article which is framed to the NLP enthusiasts in order to offer the natural language processing thesis topics”
What is Actually an NLP?
- NLP is the process of retrieving the meaning of the given sentence
- For this they use techniques & algorithms in order to extract the features
- They are also involved with the following,
- Audio capturing
- Text processing
- Conversion of audio into text
- Human-computer interaction
This is a crisp overview of the NLP system. NLP is one of the major technologies that are being used in the day to day life. Without these technologies, we could not even imagine a single scenario . In fact, they minimized the time of human beings by means of spelling checks, grammatical formations and most importantly they are highly capable of handling audio data . In this regard, let us have an idea of how does the NLP works in general. Shall we get into that section? Come let’s move on to that!!!
How does NLP Works?
- Unstructured Data Inputs
- Lingual Knowledge
- Domain Knowledge
- Domain Model
- Corpora Model Training
- Tools & Methods
The above listed are necessary when input is given to the model. The NLP model is in need of the above-itemized aspects to process the unstructured data in order to offer the structured data by means of parsing, stemming and lemmatization, and so on. In fact, NLP is subject to the classifications by their eminent features such as generation & understanding. Yes my dear students we are going to cover the next sections with the NLP classifications.
Classifications of NLP
- Natural Language-based Generation
- Natural Language-based Understanding
The above listed are the 2 major classifications of NLP technology . In these classifications let us have further brief explanations of the natural language-based understanding for your better understanding.
- Biometric Domains
- Spam Detection
- Opinion/Data Mining
- Entity Linking
- Named Entity Recognition
- Relationship Extraction
This is how the natural language-based understanding is sub-classified according to its functions. In recent days, NLP is getting boom in which various r esearches and projects are getting investigated and implemented successfully by our technical team. Generally, NLP processes are getting performed in a structural manner. That means they are overlays in several steps in crafting natural language processing thesis topics . Yes dears, we are going to envelop the next section with the steps that are concreted with the natural language processing.
NLP Natural Language Processing Steps
- Segmentation of Sentences
- Tokenization of Words
- PoS Tagging
- Parsing of Syntactic Contexts
- Removing of Stop Words
- Lemmatization & Stemming
- Classification of Texts
- Emotion/Sentiment Analysis
Here POS stands for the Parts of Speech . These are some of the steps involved in natural language processing. NLP performs according to the inputs given. Here you might need examples in these areas. For your better understanding, we are going to illustrate to you about the same with clear bulletin points. Come let us try to understand them.
- Let we take inputs as text & speech
- Text inputs are analyzed by “word tokenization”
- Speech inputs are analyzed by “phonetics”
In addition to that, they both are further processed in the same manner as they are,
- Morphological Analysis
- Syntactic Analysis
- Semantic Understanding
- Speech Processing
The above listed are the steps involved in NLP tasks in general . Word tokenization is one of the major which points out the vocabulary words presented in the word groups . Though, NLP processes are subject to numerous challenges. Our technical team is pointed out to you the challenges involved in the current days for a better understanding. Let’s move on to the current challenges sections.
Before going to the next section, we would like to highlight ourselves here. We are one of the trusted crew of technicians who are dynamically performing the NLP-based projects and researches effectively . As the matter of fact, we are offering so many successful projects all over the world by using the emerging techniques in technology. Now we can have the next section.
Current Challenges in NLP
- Context/Intention Understanding
- Voice Ambiguity/Vagueness
- Data Transformation
- Semantic Context Extracting
- Word Phrase Matching
- Vocabulary/Terminologies Creation
- PoS Tagging & Tokenization
The above listed are the current challenges that get involved in natural language processing. Besides, we can overcome these challenges by improving the NLP model by means of their performance. On the other hand, our technical experts in the concern are usually testing natural language processing approaches to abolish these constraints.
In the following passage, our technical team elaborately explained to you the various natural language processing approaches for the ease of your understanding. In fact, our researchers are always focusing on the students understanding so that they are categorizing each and every edge needed for the NLP-oriented tasks and approaches . Are you interested to know about that? Now let’s we jump into the section.
Different NLP Approaches
Domain Model-based Approaches
- Loss Centric
- Feature Centric
- Pre-Training
- Pseudo Labeling
- Data Selection
- Model + Data-Centric
Machine Learning-based Approaches
- Association
- K-Means Clustering
- Anomalies Recognition
- Data Parsing
- Regular Emotions/Expressions
- Syntactic Interpretations
- Pattern Matching
- BFS Co-location Data
- BERT & BioBERT
- Decision Trees
- Logistic Regression
- Linear Regression
- Random Forests
- Support Vector Machine
- Gradient-based Networks
- Convolutional Neural Network
- Deep Neural Networks
Text Mining Approaches
- K-nearest Neighbor
- Naïve Bayes
- Predictive Modeling
- Association Rules
- Classification
- Document Indexing
- Term & Inverse Document Frequency
- Document Term Matrix
- Distribution
- Keyword Frequency
- Term Reduction/Compression
- Stemming/lemmatization
- Tokenization
- NLP & Log Parsing
- Text Taxonomies
- Text Classifications
- Text Categorization
- Text Clustering
The above listed are the 3 major approaches that are mainly used for natural languages processing in real-time . However, there are some demerits and merits are presented with the above-listed approaches. It is also important to know about the advantages and disadvantages of the NLP approaches which will help you to focus on the constraints and lead will lead you to the developments. Shall we discuss the pros and cons of NLP approaches? Come on, guys!
Advantages & Disadvantages of NLP Approaches
- Effortless Debugging
- Effective Precisions
- Multi-perspectives
- Short Form Reading
- Ineffective Parsing
- Poor Recalls
- Excessive Skills
- Low Scalability
- Speed Processes
- Resilient Results
- Effective Documentation
- Better Recalls
- High Scalability
- Narrow Understanding
- Poor in Reading Messages
- Huge Annotations
- Complex in Debugging
The foregoing passage conveyed to you the pros and cons of two approaches named machine learning and text mining. The best approach is also having pros and cons. If you do want further explanations or clarifications on that you can feel free to approach our researchers to get benefit from us. Generally, NLP models are trained to perform every task in order to recognize the inputs with latest natural language processing project ideas . Yes, you people guessed right! The next section is all about the training models of the NLP.
Training Models in NLP
- Scratch dataset such as language-specific BERTs & multi-linguistic BERT
- These are the datasets used in model pre-training
- Auxiliary based Pre-Training
- It is the additional data tasks used for labeled adaptive pre-training
- Multi-Phase based Pre-Training
- Domain & broad tasks are the secondary phases of pre-training
- Unlabeled data sources make differences in the multiphase pre-training
- TAPT, DAPT, AdaptaBERT & BioBERT are used datasets
As this article is named as natural language processing thesis topics , here we are going to point out to you the latest thesis topics in NLP for your reference. Commonly, a thesis is the best illustration of the projects or researches done in the determined areas. In fact, they convey the researchers’ perspectives & thoughts to the opponent by the effective structures of the thesis. If you are searching for thesis writing assistance then this is the right platform, you can surely approach our team at any time.
In the following passage, we have itemized some of the latest thesis topics in NLP . We thought that it would help you a lot. Let’s get into the next section. As this is an important section, you are advised to pay your attention here. Are you really interested in getting into the next section? Come let us also learn them.
Latest Natural Language Processing Thesis Topics
- Cross & Multilingual based NLP Methods
- Multi-modal based NLP Methodologies
- Provocative based NLP Systems
- Graph oriented NLP Techniques
- Data Amplification in NLP
- Reinforcement Learning based NLP
- Dialogue/Voice Assistants
- Market & Customer Behavior Modeling
- Text Classification by Zero-shot/Semi-supervised Learning & Sentiment Analysis
- Text Generation & Summarization
- Relation & Knowledge Extraction for Fine-grained Entity Recognition
- Knowledge & Open-domain based Question & Answering
These are some of the latest thesis topics in NLP . As the matter of fact, we have delivered around 200 to 300 thesis with fruitful outcomes. Actually, they are very innovative and unique by means of their features. Our thesis writing approaches impress the institutes incredibly. At this time, we would like to reveal the future directions of the NLP for the ease of your understanding.
How to select the best thesis topics in NLP?
- See the latest IEEE and other benchmark papers
- Understand the NLP Project ideas recently proposed
- Highlight the problems and gaps
- Get the future scope of each existing work
Come let’s move on to the next section.
Future Research Directions of Natural Language Processing
- Logical Reasoning Chains
- Statistical Integrated Multilingual & Domain Knowledge Processing
- Combination of Interacting Modules
On the whole, NLP requires a better understanding of the texts. In fact, they understand the text’s meaning by relating to the presented word phrases. Conversion of the natural languages in reasoning logic will lead NLP to future directions. By allowing the modules to interact can enhance the NLP pipelines and modules. So far, we have come up with the areas of natural language processing thesis topics and each and every aspect that is needed to do a thesis. If you are in dilemma you could have the valuable opinions of our technical experts.
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Deep Learning in Natural Language Processing
- Li Deng 0 ,
AI Research at Citadel , Chicago, USA
You can also search for this editor in PubMed Google Scholar
Tsinghua University , Beijing, China
Provides an up-to-date and comprehensive survey of deep learning research and its applications in natural language processing
Covers all key tasks and techniques of natural language processing
Includes contributions written by leading researchers in the respective fields
109k Accesses
201 Citations
30 Altmetric
- Table of contents
About this book
Editors and affiliations, about the editors, bibliographic information, buying options.
- ISBN: 978-981-10-5209-5
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- ISBN: 978-981-13-3848-9
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- ISBN: 978-981-10-5208-8
This is a preview of subscription content, access via your institution .
Table of contents (11 chapters)
Front matter, a joint introduction to natural language processing and to deep learning.
- Li Deng, Yang Liu
Deep Learning in Conversational Language Understanding
- Gokhan Tur, Asli Celikyilmaz, Xiaodong He, Dilek Hakkani-Tür, Li Deng
Deep Learning in Spoken and Text-Based Dialog Systems
- Asli Celikyilmaz, Li Deng, Dilek Hakkani-Tür
Deep Learning in Lexical Analysis and Parsing
- Wanxiang Che, Yue Zhang
Deep Learning in Knowledge Graph
- Zhiyuan Liu, Xianpei Han
Deep Learning in Machine Translation
- Yang Liu, Jiajun Zhang
Deep Learning in Question Answering
- Kang Liu, Yansong Feng
Deep Learning in Sentiment Analysis
- Duyu Tang, Meishan Zhang
Deep Learning in Social Computing
- Xin Zhao, Chenliang Li
Deep Learning in Natural Language Generation from Images
- Xiaodong He, Li Deng
Epilogue: Frontiers of NLP in the Deep Learning Era
Back matter.
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence.
The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
- Deep Learning
- Natural Language Processing
- Recurrent Neural Networks
- Speech Language Understanding
- Dialogue Systems
- Machine Translation
- Knowledge Graph
- Question Answering
- Sentiment Analysis
- Social Computing
- Natural Language Generation
- Image Captioning
- Neural Embedding
- Sequence-to-Sequence
- Attention Models
Li Deng is the Chief Artificial Intelligence Officer of Citadel since May 2017. Prior to Citadel, he was the Chief Scientist of AI, the founder of Deep Learning Technology Center, and Partner Research Manager at Microsoft. Prior to Microsoft, he was a tenured full professor at the University of Waterloo in Ontario, Canada as well as teaching and conducting research at MIT (Cambridge), ATR (Kyoto, Japan) and HKUST (Hong Kong). He is a Fellow of the IEEE, a Fellow of the Acoustical Society of America, and a Fellow of the ISCA. He has also been an Affiliate Professor at University of Washington since 2000. He was an elected member of Board of Governors of the IEEE Signal Processing Society, and was Editors-in-Chief of IEEE Signal Processing Magazine and of IEEE/ACM Transactions on Audio, Speech, and Language Processing (2008-2014), for which he received the IEEE SPS Meritorious Service Award. In recognition of the pioneering work on disrupting speech recognition industry using large-scale deep learning, he received the 2015 IEEE SPS Technical Achievement Award for “Outstanding Contributions to Deep Learning and to Automatic Speech Recognition." He also received numerous best paper and patent awards for the contributions to artificial intelligence, machine learning, natural language processing, information retrieval, multimedia signal processing, and speech processing. He is an author or co-author of six technical books.
Yang Liu is an associate professor at the Department of Computer Science and Technology, Tsinghua University. He received his PhD degree from the Chinese Academy of Sciences Institute of Computing Technology in 2007. His research focuses on natural language processing and machine translation. He has published over 50 papers in leading NLP/AI journals and conferences such as Computational Linguistics, ACL, AAAI, EMNLP, and COLING. He won the COLING/ACL 2006 Meritorious Asian NLP Paper Award and the National Science and Technology Progress Award second prize. He served as Associate Editor of ACM TALLIP, ACL 2014 tutorial co-chair, ACL 2015 local arrangement co-chair, IJCAI 2016 senior PC, ACL 2017 area co-chair, EMNLP 2016 area co-chair, SIGHAN information officer, and the general secretary of the Computational Linguistics Technical Committee of Chinese Information Processing Society.
Book Title : Deep Learning in Natural Language Processing
Editors : Li Deng, Yang Liu
DOI : https://doi.org/10.1007/978-981-10-5209-5
Publisher : Springer Singapore
eBook Packages : Computer Science , Computer Science (R0)
Copyright Information : Springer Nature Singapore Pte Ltd. 2018
Hardcover ISBN : 978-981-10-5208-8 Published: 31 May 2018
Softcover ISBN : 978-981-13-3848-9 Published: 16 December 2018
eBook ISBN : 978-981-10-5209-5 Published: 23 May 2018
Edition Number : 1
Number of Pages : XVII, 329
Topics : Artificial Intelligence , Natural Language Processing (NLP) , Probability and Statistics in Computer Science
Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI —concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.
Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in sentence structure—these just a few of the irregularities of human language that take humans years to learn, but that programmers must teach natural language-driven applications to recognize and understand accurately from the start, if those applications are going to be useful.
Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it's ingesting. Some of these tasks include the following:
- Speech recognition , also called speech-to-text, is the task of reliably converting voice data into text data. Speech recognition is required for any application that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the way people talk—quickly, slurring words together, with varying emphasis and intonation, in different accents, and often using incorrect grammar.
- Part of speech tagging , also called grammatical tagging, is the process of determining the part of speech of a particular word or piece of text based on its use and context. Part of speech identifies ‘make’ as a verb in ‘I can make a paper plane,’ and as a noun in ‘What make of car do you own?’
- Word sense disambiguation is the selection of the meaning of a word with multiple meanings through a process of semantic analysis that determine the word that makes the most sense in the given context. For example, word sense disambiguation helps distinguish the meaning of the verb 'make' in ‘make the grade’ (achieve) vs. ‘make a bet’ (place).
- Named entity recognition, or NEM, identifies words or phrases as useful entities. NEM identifies ‘Kentucky’ as a location or ‘Fred’ as a man's name.
- Co-reference resolution is the task of identifying if and when two words refer to the same entity. The most common example is determining the person or object to which a certain pronoun refers (e.g., ‘she’ = ‘Mary’), but it can also involve identifying a metaphor or an idiom in the text (e.g., an instance in which 'bear' isn't an animal but a large hairy person).
- Sentiment analysis attempts to extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text.
- Natural language generation is sometimes described as the opposite of speech recognition or speech-to-text; it's the task of putting structured information into human language.
See the blog post “ NLP vs. NLU vs. NLG: the differences between three natural language processing concepts ” for a deeper look into how these concepts relate.
Python and the Natural Language Toolkit (NLTK)
The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs.
The NLTK includes libraries for many of the NLP tasks listed above, plus libraries for subtasks, such as sentence parsing, word segmentation, stemming and lemmatization (methods of trimming words down to their roots), and tokenization (for breaking phrases, sentences, paragraphs and passages into tokens that help the computer better understand the text). It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text.
Statistical NLP, machine learning, and deep learning
The earliest NLP applications were hand-coded, rules-based systems that could perform certain NLP tasks, but couldn't easily scale to accommodate a seemingly endless stream of exceptions or the increasing volumes of text and voice data.
Enter statistical NLP, which combines computer algorithms with machine learning and deep learning models to automatically extract, classify, and label elements of text and voice data and then assign a statistical likelihood to each possible meaning of those elements. Today, deep learning models and learning techniques based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) enable NLP systems that 'learn' as they work and extract ever more accurate meaning from huge volumes of raw, unstructured, and unlabeled text and voice data sets.
For a deeper dive into the nuances between these technologies and their learning approaches, see “ AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference? ”
Natural language processing is the driving force behind machine intelligence in many modern real-world applications. Here are a few examples:
- Spam detection: You may not think of spam detection as an NLP solution, but the best spam detection technologies use NLP's text classification capabilities to scan emails for language that often indicates spam or phishing. These indicators can include overuse of financial terms, characteristic bad grammar, threatening language, inappropriate urgency, misspelled company names, and more. Spam detection is one of a handful of NLP problems that experts consider 'mostly solved' (although you may argue that this doesn’t match your email experience).
- Machine translation: Google Translate is an example of widely available NLP technology at work. Truly useful machine translation involves more than replacing words in one language with words of another. Effective translation has to capture accurately the meaning and tone of the input language and translate it to text with the same meaning and desired impact in the output language. Machine translation tools are making good progress in terms of accuracy. A great way to test any machine translation tool is to translate text to one language and then back to the original. An oft-cited classic example: Not long ago, translating “ The spirit is willing but the flesh is weak” from English to Russian and back yielded “ The vodka is good but the meat is rotten .” Today, the result is “ The spirit desires, but the flesh is weak ,” which isn’t perfect, but inspires much more confidence in the English-to-Russian translation.
- Virtual agents and chatbots: Virtual agents such as Apple's Siri and Amazon's Alexa use speech recognition to recognize patterns in voice commands and natural language generation to respond with appropriate action or helpful comments. Chatbots perform the same magic in response to typed text entries. The best of these also learn to recognize contextual clues about human requests and use them to provide even better responses or options over time. The next enhancement for these applications is question answering, the ability to respond to our questions—anticipated or not—with relevant and helpful answers in their own words.
- Social media sentiment analysis: NLP has become an essential business tool for uncovering hidden data insights from social media channels. Sentiment analysis can analyze language used in social media posts, responses, reviews, and more to extract attitudes and emotions in response to products, promotions, and events–information companies can use in product designs, advertising campaigns, and more.
- Text summarization: Text summarization uses NLP techniques to digest huge volumes of digital text and create summaries and synopses for indexes, research databases, or busy readers who don't have time to read full text. The best text summarization applications use semantic reasoning and natural language generation (NLG) to add useful context and conclusions to summaries.
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B.A. in Literature. May, 1984. State University of New York at Purchase.
M.A. in Linguistics. May, 1989. New York University.
Ph.D. in Linguistics. May, 1994. New York University.
Dissertation: A Unification-based Approach to GB Theory
Research Interests
Computational Lexicography ( Comlex Syntax , Nomlex )
Predicate Argument Structure ( GLARF , NomBank )
Machine Translation and Sentence Alignment
Corpus Annotation: NomBank , 2006 Annotation Compatibility Working Group, 2011 Content of Linguistic Annotation: Standards and Practices (CLASP) repor t
Teaching Computer Science to Pre-college Students ( musicomputation )
Information Extraction for Technical Documents
The Termolator : a terminology detection tool
Resources and Software (Released Under Apache 2.0 unless otherwise specified)
Dictionaries and Annotated Corpora
Comlex Syntax, a syntactic dictionary (Not Apache: distributed by the Linguistic Data Consortium under an LDC license)
Our Website for Comlex Syntax
LDC's Website Comlex Syntax
Nomlex (a dictionary linking noun and verb argument structure)
NomBank (annotation and dictionaries relating to noun argument structure)
GLARF (a semantic parser)
The Termolator (a terminology extraction program)
Graduate and Undergraduate Teaching
Professional service.
Secretary of ACL Special Interest Group for Annotation (SIGANN)
Proquest Corpora Database Editorial Board Member 2019-2020
Chair/Co-Chair of LAW I, II, III and IX in 2007, 2008, 2009 and 2015 in connection with SIGANN
Co-Chair of the 2014 COLING Workshop on Synchronic and Diachronic Approaches to Analyzing Technical Language
Frontiers in Corpus Annotation workshops from 2004-2006
Papers : Click Here
Work experience.
Clinical Associate Professor, Research Assistant Professor, Research Scientist, Computer Science Dept., NYU. 1993 to Present.
Adjunct Professor. Linguistics Department. Montclair State University. Graduate. Natural Language Processing 2014.
Adjunct Assistant Professor, Linguistics Dept., NYU. Graduate Syntax. 1995 and 1996.
Supplemental Employee. IBM T.J. Watson Research Center.
1989-1990 (with Ezra Black)
1991-1992 (with David Johnson)
Computational Linguistics Consultant for various organizations throughout the world
Dissertations Committees
- Yusuke Shinyama, 2007, Being Lazy and Preemptive at Learning toward Information Extraction
- Shasha Liao, 2012, Acquiring information from wider scope to improve event extraction
- Xiang Li, 2016, Improving Knowledge Base Population with Information Extraction
- Maria Pershina, 2016 Graph-based Approaches to Resolve Entity Ambiguity
- Cai kao, 2016 Improving Event Extraction: Casting a Wider Net
- Thien Huu Nguyen, 2017, Deep Learning for Information Extraction
- Lisheng Fu, 2019, Enhanced Representations for Relations by Multi-task Learning
- Anabela Barreiro, 2009, Universidade do Porto, Dissertation: Make it simple with paraphrases: automated paraphrasing for authoring aids and machine translation
- John Ortega, 2021, Universitat d'Alacant, Fuzzy-Match Repair in Computer-Aided Translation Using Black-Box Machine Translation
Curriculum Vita, Research Statement, Teaching Statement and Publication List (pdfs)
Full Curriculum Vitae
Short (1 Page) Vitae
Research Statement
Teaching Statement
Publication List
Download Papers in PDF format
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Related Activities
Cornell NLP Computational Linguistics Lab Machine Learning at Cornell
Cornell researchers in natural language processing are interested in computational models of human language and machine learning, applying a computational lens to a broad set of projects in the areas of linguistic analysis, natural language understanding systems, social science, and humanities.
Representing Cornell's two campuses and several university departments, NLP scholars use computational methods to dig deeper into the words we use and apply that analysis to a wide range of topics – from online trolling and bias in tennis reporting, to the language of both persuasion and betrayal.
Contributing Faculty and Researchers
- Claire Cardie
- Morten Christiansen
- Cristian Danescu-Niculescu-Mizil
- Lillian Lee
- Michael Macy
- David Mimno
- Matthew Wilkens
Get Involved
Summer internships.
- Graduate Studies
- Full-Time Positions
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- ISI Seminars and Events
- ISI: Meet Our Researchers
- Getting to ISI
Summer 2022 Internships in Natural Language Processing
We are looking for interested and qualified students (graduate and undergraduate) to spend the summer working with ongoing research projects at USC/ISI on natural language processing, machine learning, statistical modeling, machine translation, creative language generation, and other areas.
These are paid internships. They will be available for a three month (12 week) period during the summer of 2022. The internships will, if possible, be held in Marina del Rey, however due to COVID-19 restrictions they may be virtual internships. If virtual, interns must nevertheless reside in the United States during the internship.
Good programming skills are required, but prior experience in natural language processing is not necessarily required. We will provide tutorials on relevant topics at the beginning of the summer.
Important dates
"The NLP summer internship will not take place in 2023 . Please stay tuned for more opportunities to come!"
- 2022 Jan 28 Applications due (Beware: this date may be earlier than the date listed on the general ISI internship page ).
- 2022 Feb 28 (approx.) First acceptance notifications. The procedure may last until the end of March. We are unable to respond to requests for updates.
- 2022 May 31-August 19 Internships Begin-End
How to Apply
Please follow this link . You will be required to submit a statement and provide email addresses of up to three people who will write letters of recommendation.
Project Areas of Interest
- Low Resource Neural Machine Translation. Can we use transfer learning, curriculum learning, and active learning so that machine translation quality can be reached with volumes of training data equal to that observed by a human second language learner? We will follow our previous work as well as the latest and greatest approaches.
- Creative Dialogue . Following on from our recent work , can we use inspiration from improv comedy, soap operas, and other sources to improve creativity, grounding, and fluency in dialogue systems?
- Commonsense Reasoning. A simple story: Janice got into her car and sped off. Question: Did she press on the accelerator? An AI system needs to know a lot to answer simple questions like this. Can an AI system obtain such knowledge by reading text?
- Information Extraction. There is abundant knowledge carried in the exponentially expanding corpora of natural language texts. Yet this knowledge is mostly inaccessible to computers and overwhelming for human experts to absorb. From a strong foundation , we want to build knowledge graphs to dramatically increase the accessibility of knowledge through search engines, interactive AI agents, and medical research tools.
- Robust Interlingual Representations . Encoding various languages into a unified semantic meaning space, a.k.a an interlingual representation, is a promising direction to break the language barriers for NLP technologies. We'd like to develop a framework that incorporates advances in multilingual machine translation, pre-trained language modeling, and robust representation learning to learn interlingual representations that are robust with respect to the variety of languages, imbalanced data distributions, and language-specific data biases.
- Meta-Learning for Cross-Lingual Model Transfer . A number of technologies in large-scale pre-trained language modeling, such as BERT, mBERT, XLNet and XLM, have led to impressive successes in (interlingual) representation learning. However, how to effectively apply the learned interlingual representations to downstream tasks has largely remained under-appreciated. We'd like to investigate applying meta-learning methods to transfer the learned interlingual representations into low-resource languages to enhance multilingual NLP on a broad range of applications, including named entity recognition, coreference resolution, and machine translation. In particular, we are interested in transferring models/representations to new languages and/or domains in few-shot or even zero-shot scenarios.
- Understanding event processes. Natural language always communicates about events, and events often connect into processes due to some central goal. Given the event process "fulfilling course requirements" -> "passing qualification exams" -> "publish papers" -> "doing internships" -> "defend dissertation", does a machine understand that it leads to the central goal of "earning a degree"? And how do we efficiently teach the machine to understand the salience of events, i.e. that "defending dissertation" is much more important than "doing internships"? Does such knowledge help downstream tasks like summarization?
- Knowledge Acquisition with Indirect Supervision . Knowledge acquisition (e.g., relation extraction, entity and event typing) faces challenges including extreme label spaces, few-shot/zero-shot predictions and out-of-domain prediction. To this end, we study methods for leveraging indirect supervision signals from auxiliary tasks (e.g., natural language inference, text summarization, etx.) to foster robust and generalizable inference for knowledge acquisition. In the same context, we study methods for generating semantically rich label representations based on either gloss knowledge or structural knowledge from a well-populated lexical knowledge base, in order to better support learning with limited labels.
Research Environment
Summer internship projects are supervised by Jonathan May , Xuezhe Ma , and Muhao Chen . Interns also interact and collaborate closely with the rest of ISI's Natural Language Group. Our group's research environment includes weekly seminars and reading groups, opportunities for teaching and advising, an active program for summer students, large quantities of linguistic resources, and a supercomputing cluster completely dedicated to natural language research at USC/ISI.
USC/ISI is an academic research institute that is part of USC 's Viterbi School of Engineering ; many USC/ISI scientists hold research faculty positions in the Computer Science Department . The Natural Language Group is part of USC/ISI's Artificial Intelligence Division which carries out a wide range of artificial intelligence research.
USC/ISI is located in Marina del Rey on the Southern California coast, an excellent location convenient to beaches, restaurants, boating, bike paths, and shopping. Note: we are not located on the main campus of USC, which is near downtown LA.
Past Interns
Our summer program is well established! Past students are listed below. Several students (marked *) interned twice, and several (marked ^) joined ISI later as a PhD student, visiting PhD student, or research scientist.
- 2022: Jacqueline He (Princeton University), Jonne Sälevä (Brandeis University), Taiwei Shi (Georgia Tech)
- 2021: Shanxiu He (UCLA), Leo Zeyu Liu (University of Washington), Shira Wein (Georgetown University)
- 2020: Omar Shaikh (Georgia Tech), Ugur Yavuz (Dartmouth College), Weiqiu You (University of Pennsylvania), Naitian Zhou (University of Michigan)
- 2019: Justin Cho^ (Hong Kong Univ. of Science and Technology), Denis Emelin (Edinburgh), Zhifeng Hu (Fudan Univ.) Angelina McMillan-Major (UW), Prince Wang (UCSB), Shufan Wang (UMass)
- 2018: Ronald Cardenas (Charles University in Prague), Mozhdeh Gheini (USC), Xiaolei Huang (Univ. of Colorado), Allison Limke (Wartburg College), James Mullenbach (Georgia Tech), Xinyu Wang (CMU)
- 2017: Yining Chen (Dartmouth), Leon Cheung (UCSD), Sorcha Gilroy (Edinburgh), Nelson Liu (UW), Alexandra (Sasha) Mayn (Carleton College)
- 2016: Nada Aldarrab^ (USC), Angeliki Laziradou (U. Trento), Xiang Li (U. Chicago), Sabrina Mielke (Dresden Univ. Technology), Ke Tran (U. Amsterdam)
- 2015: Callum O'Shaughnessy (Queens University), Sudha Rao (Maryland), Wenduan Xu (Cambridge), Barret Zoph (USC)
- 2014: Julian Schamper (Aachen), Eunsol Choi (Washington), Allen Schmaltz (Harvard), Matic Horvat (Cambridge)
- 2013: Daniel Bauer* (Columbia), Fabienne Braune (Stuttgart), Jackie Lee (MIT), Elliot Meyerson (Wesleyan), Arvind Neelakantan (Columbia/UMass), Malte Nuhn (Aachen)
- 2012: Jacob Andreas (Columbia), Daniel Bauer (Columbia), Karl Moritz Hermann (Oxford), Bevan Jones (Edinburgh/Macquarrie), Nathan Schneider (CMU), Ada Wan (CUNY).
- 2011: Licheng Fang (Rochester), Sravana Reddy* (Chicago), Xuchen Yao (JHU).
- 2010: Yoav Goldberg (Ben Gurion, Israel), Ann Irvine (Hopkins), Sravana Reddy (Chicago), Alexander "Sasha" Rush (MIT).
- 2009: Michael Auli (University of Edinburgh), Paramveer Dhillon (Penn), Erica Greene^ (Haverford), Adam Pauls (UC Berkeley)
- 2008: Amittai Axelrod (University of Washington), John DeNero (UC Berkeley), Kyle Gorman (Penn Linguistics), Catalin Tirnauca (Universitat Rovira i Virgili)
- 2007: Michael Bloodgood (Delaware), Jennifer Gillenwater (Rice University), Carmen Heger (Dresden), Wei Ho (Princeton).
- 2006: Joseph Turian (NYU), Chenhai Xi (Pitt), Victoria Fossum*^ (Michigan), Liang Huang*^ (Penn), Jason Riesa*^ (JHU), Oana-Diana Postolache^ (Saarland).
- 2005: Victoria Fossum (Michigan), Mark Hopkins* (UCLA), Liang Huang (Penn), Behrang Mohit (Pitt), Preslav Nakov (Berkeley), Jason Riesa (JHU), Hao Zhang (Rochester).
- 2004: Madhur Ambastha (Rochester), Michel Galley* (Columbia), David Kauchak (UCSD).
- 2003: Michel Galley (Columbia), Mark Hopkins (UCLA), Beata Klebanov (Hebrew University), Ana-Maria Popescu (University of Washington), Lara Taylor (UCSD).
- 2002: Chris Ackerman (USC), Emil Ettelaie (USC), Yuling Hsueh (USC), John Lee (Waterloo/MIT), Bo Pang (Cornell)
- 2001: Abdessamad Echihabi (USC), Hal Daume III^ (CMU), Michael Laszlo (Waterloo), Dragos Stefan Munteanu^ (Iowa), Rebecca Rees (BYU), Radu Soricut^ (Iowa)
- 1994-2000: Estibaliz Amorrortu, Vasileios Hatzivassiloglou (Columbia), Michael Jahr (Stanford), Larry Kite (USC), Magdalena Romera (USC), Maki Watanabe (USC).
Intern Publications
We always aim to solve interesting and novel scientific problems, and to publish the results in the best conferences. Sample papers that have come from past student internships:
- "Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects", (J. Mullenbach, J. Gordon, N. Peng and J. May), Proc. EMNLP, 2019.
- "What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis", (X. Huang, J. May and N. Peng), Proc. EMNLP, 2019.
- "A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages", (R. Cardenas, Y. Lin, H. Ji, J. May), Proc. NAACL, 2019.
- "Recurrent Neural Networks as Weighted Language Recognizers" (Y. Chen, S. Gilroy, A. Maletti, J. May, and K. Knight), Proc. NAACL, 2018. Outstanding Paper Award .
- "Biomedical Event Extraction using Abstract Meaning Representation" (S. Rao, D. Marcu, K. Knight, and H. Daume), Proc. BioNLP Workshop, ACL, 2017.
- "Unsupervised Neural Hidden Markov Models" (K. Tran, Y. Bisk, A. Vaswani, D. Marcu, and K. Knight), Proceedings of the EMNLP Workshop on Structured Prediction, 2016.
- "Multi-Source Neural Translation" (B. Zoph and K. Knight), Proceedings of NAACL 2016.
- "Extracting Structured Scholarly Information from the Machine Translation Literature" (E. Choi, M. Horvat, J. May, K. Knight, D. Marcu), Proceedings of LREC 2016.
- "Cipher Type Detection" (Malte Nuhn and Kevin Knight), Proceedings of EMNLP 2014.
- "Mapping between English Strings and Reentrant Semantic Graphs" (F. Braune, D. Bauer, and K. Knight), Proceedings of LREC 2014.
- "Parsing Graphs with Hyperedge Replacement Grammars" (D. Chiang, J. Andreas, D. Bauer, K.-M. Hermann, B. Jones and K. Knight), Proceedings of ACL 2013.
- "Learning Whom to Trust with MACE" (D. Hovy, T. Berg-Kirkpatrick, A. Vaswani, and E. Hovy), Proceedings of NAACL 2013.
- "Semantics-Based Machine Translation with Hyperedge Replacement Grammars" (B. Jones, J. Andreas, D. Bauer, K.-M. Hermann, K. Knight), Proceedings of COLING 2012.
- "Feature-Rich Language-Independent Syntax-Based Alignment for Statistical Machine Translation" (J. Riesa, A. Irvine, D. Marcu), Proceedings of EMNLP 2011.
- "Language-independent parsing with empty elements" (S. Cai, D. Chiang, Y. Goldberg), Proceedings of ACL 2011.
- "Automatic Analysis of Rhythmic Poetry with Applications to Generation and Translation" (E. Greene, T. Bodrumlu, K. Knight), Proceedings of EMNLP 2010.
- "Efficient optimization of an MDL-inspired objective function for unsupervised part-of-speech tagging" (A. Vaswani, A. Pauls, D. Chiang), Proceedings of ACL 2010.
- "Unsupervised Syntactic Alignment with Inversion Transduction Grammars" (A. Pauls, D. Klein, D. Chiang, K. Knight), Proceedings of NAACL 2010.
- "Bayesian Inference for Finite-State Transducers" (D. Chiang, J. Graehl, K. Knight, A. Pauls, S. Ravi), Proceedings of NAACL 2010.
- "Binarization of Synchronous Context-Free Grammars" (L. Huang, H. Zhang, D. Gildea, K. Knight), Computational Linguistics, 2009.
- "Fast Consensus Decoding over Translation Forests" (J. DeNero, D. Chiang, and K. Knight). Proceedings of ACL 2009.
- "Forest Rescoring: Faster Decoding with Integrated Language Models" (L. Huang and D. Chiang), Proceedings of ACL 2007.
- "Scalable Inference and Training of Context-Rich Syntactic Models" (M. Galley, J. Graehl, K. Knight, D. Marcu, S. DeNeefe, W. Wang, and I. Thayer), Proceedings of ACL 2006, poster session.
- "Synchronous Binarization for Machine Translation" (H. Zhang, L. Huang, D. Gildea, K. Knight), Proceedings of NAACL 2006.
- "Statistical Syntax-Directed Translation with Extended Domain of Locality" (L. Huang, K. Knight, A. Joshi), Proceedings of the Conference of the Association for Machine Translation in the Americas (AMTA-06).
- "Building an English-Iraqi Arabic Machine Translation System for Spoken Utterances with Limited Resources" (J. Riesa, B. Mohit, K. Knight, D. Marcu), Proceedings of Interspeech 2006.
- "Text Simplification for Information Seeking Applications" (B. Beigman Klebanov, K. Knight, D. Marcu), In: On the Move to Meaningful Internet Systems, eds. R. Meersman and Z. Tari, Lecture Notes in Computer Science (3290), Springer-Verlag, 2004.
- "What's in a Translation Rule?" (M. Galley, M. Hopkins, K. Knight, D. Marcu), Proceedings of NAACL 2004.
- "Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and Generating New Sentences" (B. Pang, K. Knight, and D. Marcu), Proceedings of NAACL 2003.
- "Using a Large Monolingual Corpus to Improve Translation Accuracy" (R. Soricut, K. Knight, and D. Marcu), Proceedings of the 6th Association for Machine Translation in the Americas Conference (AMTA-2002).
- "Processing Comparable Corpora With Bilingual Suffix Trees" (D. Munteanu and D. Marcu), Proceedings of EMNLP 2002.
- "A Noisy-Channel Model for Document Compression" (H. Daume III and D. Marcu), Proceedings of ACL 2002.
- "An Unsupervised Approach to Recognizing Discourse Relations" (D. Marcu and A. Echihabi), Proceedings of ACL 2002.
- "Fast Decoding and Optimal Decoding for Machine Translation" (U. Germann, M. Jahr, K. Knight, D. Marcu, and K. Yamada), Proceedings of ACL 2001. ACL Best Paper award.
- "An Empirical Study in Multilingual Natural Language Generation: What Should a Text Planner Do?" (D. Marcu, L. Carlson, and M. Watanabe), The 1st International Conference on Natural Language Generation INLG'2000, Mitzpe Ramon, Israel, 2000.
- "Experiments in Constructing a Corpus of Discourse Trees" (D. Marcu, E. Amorrortu, and M. Romera), ACL'99 Workshop on Standards and Tools for Discourse Tagging, Univ. Maryland, 1999.
- "Two-Level, Many-Paths Generation," (K. Knight and V. Hatzivassiloglou), Proceedings of ACL 1995.
Frequently Asked Questions
Q: Should I include anything in my application in addition to a statement of purpose and CV (e.g. sample publications, awards, certificates, etc.)?
A: No. We will discard, unread, any supplemental material . We only read your statement of purpose, CV, and letters sent by your recommenders.
Q: Is the salary enough for a decent life in westside LA? What will the exact salary be?
A: Yes, of course! Our internship compensation is competitive with industrial internships. Housing is generally expensive in this area (because it's safe, beautiful, and close to the ocean), but definitely affordable with the salary we offer. The exact amount is yet to be determined (and will be stated on the offer letter), but again it will be enough for a decent life for 3 months.
Q: Where will I live during the internship?
A: Apartments in Marina del Rey proper can be very expensive but short-term rentals are often available in Palms, Culver City, Del Rey, Venice, and Santa Monica (these are names of neighborhoods and/or towns nearby and will help your search). You might want to consider teaming up with other interns; we will put you in touch. Increasingly, interns find housing near USC's main campus and take the free daily shuttle in to ISI. The method of finding housing changes over time; as of this writing it is frequently done via social media platforms: try looking for 'USC housing' groups on Facebook or WeChat.
Q: During the internship, can I go to a conference for a week or so? Or a short vacation?
A: Conferences are definitely OK, especially when you have a paper there, but in any case there should be at least 12 weeks of work here (otherwise it's hard to get anything sizable done). We generally discourage vacations over a week during the internship.
Q: My summer break does not line up with your schedule. Will you still consider my application?
A: We can accommodate early/late arrivals/departures of no more than two weeks, as long as you complete 12 weeks of work here (see above). This should be sufficient to accommodate US semester and quarter systems.
Q: Can I keep working on the projects after going back to my own school?
A: In general yes, especially when you are writing up a paper on the topic. Most likely you will be logging in remotely to work on our machines.
Q: Can I survive without a car here?
A: For three months, definitely yes. Many of our past interns did not own a car while here, and they either bike or take a bus to ISI. Unlike other parts of LA, we do have reliable buslines systems here in this area. The famous Santa Monica "big blue" buses serve UCLA, Santa Monica, Palms, Venice, ISI, and LAX, and Culver City bus lines serve Culver City, Venice, ISI, and LAX, and LA Metro buses and trains can take you to downtown LA and beyond. Additionally, a free shuttle runs during work days between USC's main campus and ISI; this is especially convenient because many interns find lodging there. Furthermore, LAX is very close to ISI (10 minutes by bus) so air travel is convenient.
Q: Are international students eligible to apply?
A: Yes, we do take on international students (see past interns list). For international students currently studying in the United States (F-1 holders), we will help you get an OPT or CPT status on top of your F-1, which is generally straighforward. CPT is largely preferred because it takes much shorter time to get approved but requires you to register for (at least) one unit in the summer. OPT usually takes 2-3 months to get approved, but you don't need to register any unit. For details about CPT/OPT, please consult your school's international student office. For international students currently studying outside the United States, we will help you get a J-1 visa. However, if you do not already have a social security number you should plan to come to the United States at least two weeks before you are to begin working in order to have enough time to obtain work authorization.
If we are forced to conduct the internship program remotely (e.g. due to COVID-19) you must still reside in the United States for the duration of the internship.
Q: I have other plans in the summer, so can I intern during Fall or Spring?
A: No, we only take summer interns (and they have to start within two weeks of our official start date).
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Department of Computer Science
Natural language processing and text mining
The natural language processing and text mining group is one of the smallest groups in the Department but over the years has consistently achieved high quality research outputs, attracted significant funding and trained outstanding PhD students.
Our researchers
- Sophia Ananiadou (Area Lead)
- Riza Batista-Navarro
- Goran Nenadic
- Nhung Nguyen
- Junichi Tsujii
Its roots lie in the pioneering research in NLP conducted between 1980 and 2000 at the Centre for Computational Linguistics of UMIST (one of the two founding universities of The University of Manchester). Since 2004, the Group has focussed its activities around the interplay of NLP and TM. Its pre-eminence in TM was recognised in 2004 by the award of major funding from JISC/BBSRC/EPSRC to set up the world’s first publicly-funded National Centre for Text Mining (NaCTeM), which immediately became an international centre of text mining expertise. NaCTeM’s ethos has always been to drive forward the state of the art in research, with results then being fed into the development of tools, services and resources (annotated corpora, computational lexica) of benefit to the wider research community.
NaCTeM researchers have excelled in community shared tasks and challenges, notably in BioCreAtIvE III, IV and V, in BioNLP 2011 and 2013 (for the most complex task of event extraction) and most recently obtained two first places in tasks of the 5th CL-SciSumm Shared Task 2019. Moreover, NaCTeM’s participation in DARPA’s $45m Big Cancer Mechanism initiative, in a consortium led by the University of Chicago, saw it produce in 2015 the top performing system for extracting information to support cancer pathway modelling. NaCTeM’s academic and industrial research projects range over many domains from biology and biomedicine to biodiversity, toxicology, neuroscience, materials, history, social sciences, insurance, and health and safety in the construction industry, with funding coming from EPSRC, ESRC, MRC, AHRC, Wellcome Trust, NIH, Pacific Life Re, Lloyd’s Register Foundation, AstraZeneca, DARPA, EC Horizon 2020, JST, the cosmetics and extracts industry, among others.
Applications arising from such research include Thalia , a semantic search engine over more than 20m biomedical abstracts; Facta+ , to find unsuspected associations in the biomedical literature; HoM , allowing semantic search of historical medical and public health archives; and RobotAnalyst , supporting the hitherto laborious screening stage of systematic reviewing through active learning techniques. NaCTeM also collaborates closely with the Artificial Intelligence Research Center , National Institute of Advanced Industrial Science and Technology, Japan.
The research group leads the UK healthcare text analytics network ( Healtex ), is part of the Farr Institute’s Health eResearch Centre (HeRC) and has pioneered the creation of the ACL SIGBIOMED special interest group featuring the BioNLP workshops since 2002.
Part of the research group has also delved into text mining applied to social sciences. Our work on social media analytics underpinned by text mining techniques (eg: text classification, sentiment analysis, topic modelling, named entity recognition) has been providing insights into the social "pulse" on issues ranging from customer satisfaction, through to fair work and human rights. Additionally, we seek to enhance civic engagement with our work on the text mining-based analysis of Parliamentary data (eg: UK Hansard archives).
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Artificial intelligence and natural language processing
Making sense of human communication is at the heart of our work in natural language processing and Artificial Intelligence. Research in these areas, and in particular the success of deep learning, is leading to unprecedented improvements in applications such as text understanding, information retrieval and human language interfaces.
Our research also aims to develop a deeper understanding of how humans use language, which we investigate with our research in natural language generation, ambiguity analysis and dialogue systems. Our research applies the same techniques to understanding music, to automatically generate adaptive soundtracks for computer games.
Entry requirements
Minimum 2:1 undergraduate degree (or equivalent). If you are not a UK citizen, you may need to prove your knowledge of English .
Potential research projects
- Natural language generation
- Information extraction from texts
- Dialogue systems
- Automatic music composition
- Natural language processing in educational applications
- Textual similarity
Potential supervisors
- Dr Alistair Willis
Some of our research students are funded via EPSRC DTP and the STEM Faculty; others are self-funded.
For detailed information about fees and funding, visit Fees and studentships .
To see current funded studentship vacancies across all research areas, see Current studentships .
- Artificial Intelligence and Natural Language Processing Research Group
- School of Computing and Communications
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Email: stem-cc-phd
If you’re interested in applying for this research topic, please take a look at the application process .
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Undergraduate Research Topics; AB Junior Research Workshops; COS IW/ Thesis FAQs; Past Independent Work Seminar Offerings; Undergraduate Program FAQ; Certificate Program. ... Natural Language Processing. Natural Language Processing. Associated Faculty. Sanjeev Arora; Danqi Chen; Adji Bousso Dieng; Karthik Narasimhan; Associated Graduate Students.
Independent Research Topics Natural algorithms (flocking, swarming, social networks, etc). Sublinear algorithms; Self-improving algorithms; Markov data structures; Danqi Chen, Room 412. Available for single-term IW and senior thesis advising, 2022-2023. Research areas: Natural Language Processing, Deep Learning
Natural Language Processing or NLP is an AI component concerned with the interaction between human language and computers. When you are a beginner in the field of software development, it can be tricky to find NLP projects that match your learning needs. So, we have collated some examples to get you started.
The best known natural language processing tool is GPT-3, from OpenAI, which uses AI and statistics to predict the next word in a sentence based on the preceding words. NLP practitioners call ...
Cornell natural language processing scholars win Best Paper at top conference; ... Cornell Bowers CIS Undergraduate Research Experience (BURE) Independent Research (CS 4999) Student Groups; UGrad Events; Undergraduate Learning Center; UGrad Course Staff Info; M Eng. Admissions. The Review Process;
Center for Language and Speech Processing CLSP conducts research across a broad spectrum of fundamental and applied topics including acoustic processing, automatic speech recognition, big data, cognitive modeling, computational linguistics, information extraction, machine learning, machine translation, and text analysis. Visit site
List of Research Topics Ideas for Natural language processing. Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse A Taxonomy for Deep Learning in Natural Language Processing Prediction of severe chest injury using natural language processing from the electronic health record
Natural Language Processing (NLP) Python Text Analysis Fundamentals: Parts 1-2 March 8, 2023, 2:00pm This two-part workshop series will prepare participants to move forward with research that uses text analysis, with a special focus on humanities and social science applications. Log in via CalNet to register. Read more Peter Amerkhanian
Research Areas in Natural Language Topics Anomaly and also Detection of reuse Biomedical text mining Computer assisted reviewing Computer-human dialogue systems Computer vision and also NLP Controlled natural language Deep linguistic processing Efficient Information also extraction techniques Events and Semantics of time
Generally, natural language processing is the sub-branch of Artificial Intelligence (AI). Natural language processing is otherwise known as NLP. It is compatible in dealing with multi-linguistic aspects and they convert the text into binary formats in which computers can understand it.
Natural language processing helps computers comprehend, decipher, and manipulate text and spoken words—bridging the gap between human language and machine communication. Topics Faculty & Researchers Centers & Labs Topics Automatic speech recognition Explainable models Entity understanding Grounded language Information extraction Question answering
Almost everything in Natural Language Processing (NLP) should be considered to be done in multiple languages, starting from English. Where the complexity of an NLP task can differ across languages, we consider English as the first choice, and then only tend to move to other languages.
One of its main research areas is Natural Language Processing that has numerous applications in various field. This post is dedicated to all researchers interested in NLP either those that...
Li Deng, Yang Liu. Provides an up-to-date and comprehensive survey of deep learning research and its applications in natural language processing. Covers all key tasks and techniques of natural language processing. Includes contributions written by leading researchers in the respective fields. 109k Accesses.
Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI —concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics—rule-based modeling of human language ...
Hi here is some research area as information retrieval , machine translation, text correction, text identification, Cite. 4th Mar, 2012. Syeda Syadath. Ministry of Health, Sultanate of Oman. Here ...
Special Topics: Natural Language Processing (Undergraduate) or. Natural Language Processing (Graduate) Data Science and Other . Current Semester. Spring 2023 CSCI-UA.0003-001. ... Curriculum Vita, Research Statement, Teaching Statement and Publication List (pdfs) Full Curriculum Vitae .
Cornell researchers in natural language processing are interested in computational models of human language and machine learning, applying a computational lens to a broad set of projects in the areas of linguistic analysis, natural language understanding systems, social science, and humanities.
Summer 2022 Internships in Natural Language Processing We are looking for interested and qualified students (graduate and undergraduate) to spend the summer working with ongoing research projects at USC/ISI on natural language processing, machine learning, statistical modeling, machine translation, creative language generation, and other areas.
The natural language processing and text mining group is one of the smallest groups in the Department but over the years has consistently achieved high quality research outputs, attracted significant funding and trained outstanding PhD students. ... (eg: text classification, sentiment analysis, topic modelling, named entity recognition) has ...
Making sense of human communication is at the heart of our work in natural language processing and Artificial Intelligence. Research in these areas, and in particular the success of deep learning, is leading to unprecedented improvements in applications such as text understanding, information retrieval and human language interfaces.
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