Christopher David Manning (born September 18, 1965) is a computer scientist and applied linguist whose research in the areas of natural language processing, artificial intelligence and machine learning is considered highly influential. He is the current Director of the Stanford Artificial Intelligence Laboratory (SAIL).
Manning is best known for co-developing GloVe word vectors and the bilinear or multiplicative form of attention in artificial neural networks and for his books Foundations of Statistical Natural Language Processing (1999) and Introduction to Information Retrieval (2008). He is the Thomas M. Siebel Professor in Machine Learning and a professor of Linguistics and Computer Science at Stanford University. He was previously President of the Association for Computational Linguistics (2015) and he has received an honorary doctorate from the University of Amsterdam (2023). [1] [2] [3]
Manning received a BA (Hons) degree majoring in mathematics, computer science, and linguistics from the Australian National University (1989) and a PhD in linguistics from Stanford (1994), under the guidance of Joan Bresnan. [4] [5] He was an assistant professor at Carnegie Mellon University (1994–96) and a lecturer at the University of Sydney (1996–99) before returning to Stanford as an assistant professor. At Stanford, he was promoted to associate professor in 2006 and to full professor in 2012. He was elected an AAAI Fellow in 2010. [6]
Manning's linguistic work includes his dissertation Ergativity: Argument Structure and Grammatical Relations (1996), a monograph Complex Predicates and Information Spreading in LFG (1999), [7] and his work developing Universal Dependencies, [8] from which he is the namesake of Manning's Law. He has also led development of open source computational linguistics software including CoreNLP, Stanza, and GloVe. [9]
Manning's PhD students include Dan Klein, Richard Socher, and Sepandar Kamvar. [5] In 2021, he joined AIX Ventures [10] as an Investment Partner. AIX Ventures is a venture capital fund that invests in artificial intelligence startups.
Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval. It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. To this end, natural language processing often borrows ideas from theoretical linguistics. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subset of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hard problem.
The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. It was developed by Steven Bird and Edward Loper in the Department of Computer and Information Science at the University of Pennsylvania. NLTK includes graphical demonstrations and sample data. It is accompanied by a book that explains the underlying concepts behind the language processing tasks supported by the toolkit, plus a cookbook.
Daniel Klein is an American computer scientist and professor of computer science at the University of California, Berkeley. His research focuses on natural language processing and artificial intelligence.
Karen Ida Boalth Spärck Jones was a self-taught programmer and a pioneering British computer scientist responsible for the concept of inverse document frequency (IDF), a technology that underlies most modern search engines. She was an advocate for women in the field of computer science. She even came up with a slogan: "Computing is too important to be left to men." In 2019, The New York Times published her belated obituary in its series Overlooked, calling her "a pioneer of computer science for work combining statistics and linguistics, and an advocate for women in the field." From 2008, to recognize her achievements in the fields of information retrieval (IR) and natural language processing (NLP), the Karen Spärck Jones Award is awarded to a new recipient with outstanding research in one or both of her fields.
Language and Communication Technologies is the scientific study of technologies that explore language and communication. It is an interdisciplinary field that encompasses the fields of computer science, linguistics and cognitive science.
William Aaron Woods, generally known as Bill Woods, is a researcher in natural language processing, continuous speech understanding, knowledge representation, and knowledge-based search technology. He is currently a Software Engineer at Google.
Dragomir R. Radev was an American computer scientist who was a professor at Yale University, working on natural language processing and information retrieval. He also served as a University of Michigan computer science professor and Columbia University computer science adjunct professor, as well as a Member of the Advisory Board of Lawyaw.
The following outline is provided as an overview of and topical guide to natural-language processing:
Barbara J. Grosz CorrFRSE is an American computer scientist and Higgins Professor of Natural Sciences at Harvard University. She has made seminal contributions to the fields of natural language processing and multi-agent systems. With Alison Simmons, she is co-founder of the Embedded EthiCS programme at Harvard, which embeds ethics lessons into computer science courses.
In natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.
Dan Roth is the Eduardo D. Glandt Distinguished Professor of Computer and Information Science at the University of Pennsylvania.
Semantic folding theory describes a procedure for encoding the semantics of natural language text in a semantically grounded binary representation. This approach provides a framework for modelling how language data is processed by the neocortex.
Pascale Fung (馮雁) is a professor in the Department of Electronic & Computer Engineering and the Department of Computer Science & Engineering at the Hong Kong University of Science & Technology(HKUST). She is the director of the newly established, multidisciplinary Centre for AI Research (CAiRE) at HKUST. She is an elected Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for her “contributions to human-machine interactions”, an elected Fellow of the International Speech Communication Association for “fundamental contributions to the interdisciplinary area of spoken language human-machine interactions” and an elected Fellow of the Association for Computational Linguistics (ACL) for her “significant contributions toward statistical NLP, comparable corpora, and building intelligent systems that can understand and empathize with humans”.
Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch and ambiguity of natural language.
Pushpak Bhattacharyya is a computer scientist and a professor at Computer Science and Engineering Department, IIT Bombay. He served as the director of Indian Institute of Technology Patna from 2015 to 2021. He is a past president of Association for Computational Linguistics (2016–17), and Ex-Vijay and Sita Vashee Chair Professor He currently heads the Natural language processing research group Center For Indian Language Technology (CFILT) lab at IIT Bombay.
Lillian Lee is a computer scientist whose research involves natural language processing, sentiment analysis, and computational social science. She is a professor of computer science and information science at Cornell University, and co-editor-in-chief of the journal Transactions of the Association for Computational Linguistics.
Danqi Chen is a Chinese computer scientist and assistant professor at Princeton University specializing in the AI field of natural language processing (NLP). In 2019, she joined the Princeton NLP group, alongside Sanjeev Arora, Christiane Fellbaum, and Karthik Narasimhan. She was previously a visiting scientist at Facebook AI Research (FAIR). She earned her Ph.D. at Stanford University and her BS from Tsinghua University.
Bonnie Jean Dorr is an American computer scientist specializing in natural language processing, machine translation, automatic summarization, social computing, and explainable artificial intelligence. She is a professor and director of the Natural Language Processing Research Laboratory in the Department of Computer & Information Science & Engineering at the University of Florida. Gainesville, Florida She is professor emerita of computer science and linguistics and former dean at the University of Maryland, College Park, former associate director at the Florida Institute for Human and Machine Cognition,, and former president of the Association for Computational Linguistics.
Ellen Riloff is an American computer scientist currently serving as a professor at the School of Computing at the University of Utah. Her research focuses on natural language processing and computational linguistics, specifically information extraction, sentiment analysis, semantic class induction, and bootstrapping methods that learn from unannotated texts.