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John Langford is a machine learning research scientist, a field which he says “is shifting from an academic discipline to an industrial tool”.

His primary research interest is in understanding how to get human knowledge into a machine learning system in the most efficient way possible.

He works primarily in the areas of language (computational linguistics and natural language processing) and machine learning (structured prediction, domain adaptation and Bayesian inference).

He associates himself most with conferences like ACL, ICML, NIPS and EMNLP, and has over 30 conference papers (one best paper award in ECML/PKDD 2010) and 7 journal papers.

Machine Learning Group

Instead of laboriously creating a handcraftedset of filtering rules, we can use machine learning to extractpatterns that differentiate spam from ham, based solely on a collectionof messages that have been labelled as spam and ham respectively, andthen use those patterns in the filter.

It has been used to helpdetermine what information dairy farmers use in deciding which cowsto keep in their herds, been applied to bioinformatics problems such asgene interaction discovery, and been used for many other applicationssuch as mining supermarket transaction data for high profit productassociations, predicting the levels of chemicals like nitrogen and carbonin soils to aid farmers' fertilizer decisions, and processing naturallanguage to extract keywords from documents.Further details on the group can be found at:

Machine Learning Department - School of Computer Science

Her research formalizes and explicitly addresses all constraints and important challenges of these new settings, including statistical efficiency, computational efficiency, noise tolerance, limited supervision or interaction, privacy, low communication, and incentives.

Analysis of algorithms beyond the worst case and more generally identifying interesting and realistic models of computation that provide a better alternative to traditional worst-case models in a broad range of optimization problems (including problems of extracting hidden information from data).

The 20 Best Computational Linguistics Graduate Programs in the U.S.

Computational linguists create tools for important practical tasks such as machine translation, speech recognition, speech synthesis, information extraction from text, grammar checking, text mining and more.

Computational Linguistics Graduate Programs The major schools in computational linguistics typically have a strong interdisciplinary culture with the linguistics department and the computer science department and with other related departments.

Some linguistics departments offer the specialization, however at many colleges and universities the computer science (CS) department or a related department actually offers the specialization.

Some computer science departments don’t even mention a computational linguistics specialization at their website, however they actually have computer science graduate students specializing in computational linguistics along with faculty members performing research in the subject.

Computational linguistic students study subjects such as semantics, computational semantics, syntax, models in cognitive science, natural language processing systems and applications, morphology, linguistic phonetics and phonology.

Students may also study sociolinguistics, psycholinguistics, corpus linguistics, machine learning, applied text analysis, grounded models of meaning, data-intensive computing for text analysis, and information retrieval.

During their journey computational linguistic students typically take computer programming courses as well as math and statistics courses.

However, some general courses such as methods in computational linguistics teach computer programming at a level which provides students the skills to begin creating computer applications to address computational linguistics tasks.

students specializing in CL in the computer science department can take courses such as operating systems, programming languages, analysis of algorithms, natural language processing, computation and formal systems, science of data structures, machine learning, artificial intelligence, and computer architecture.

Computational linguists work for high tech companies, creating and testing models for improving or developing new software in areas such as speech recognition, grammar checkers, dictionary development and more.

NLP professionals organize and structure knowledge to perform tasks such as translation, relationship extraction, automatic summarization, sentiment analysis, text clustering and categorization, named entity recognition, text segmentation and speech recognition.

NPL systems, with their ability to analyze language for its meaning, have filled roles such as correcting grammar, automatically translating between languages, and converting speech to text.

Natural language processing provides a way for machines to communicate with people on conventional language-based terms, which makes NLP an important factor in cognitive computing.

Data scientist use natural language processing for log analysis of security models, risk management and regulatory compliance as well as price and demand forecasting.

Companies use NLP to improve the accuracy of documentation, improve the efficiency of documentation processes, and to identify the most pertinent information from large databases.

selected the graduate programs based on the quality of the program, types of courses provided, research opportunities, and faculty strength including research, awards, recognition and reputation.

Theoretical foci include: The applied work in human language technology covers areas such as: The Stanford Natural Language Processing Group includes members of the Linguistics Department and the Computer Science Department.

The Stanford NLP Group consists of faculty, postdocs, programmers and students who work together on algorithms which allow computers to process and understand human languages.

The Stanford NLP Group combines a sophisticated and deep linguistic modeling and data analysis with probabilistic machine learning and deep learning approaches to natural language processing.

They work in natural language areas such as machine translation, interactive dialog systems, summarization, information retrieval, speech and machine learning.

The department provides graduate training in core areas such as syntax, phonetics and phonology, psycholinguistics, semantics and pragmatics, fieldwork and acquisition.

The center has a focus on advanced technology for automatically analyzing a broad range of text, speech, and document data in multiple languages.

He has research interests in NLP, spoken language systems, information retrieval, machine translation, very large text databases and machine learning.

She has research interests in computational linguistics/natural language, prosody, processing, spoken dialogue systems, emotional speech, deceptive speech, text-to-scene generation, entrainment/alignment in dialogue, code-switching and speech summarization.

She is interested in text summarization, natural language generation, question answering, digital libraries, multimedia explanation and multilingual applications.

Owen Rambow, Research Scientist at the Center for Computational Learning Systems, has expertise in the areas of formal and computational models of syntax and other levels of linguistic representation as well as in modeling how people use language to achieve communicative objectives.

The Graduate program includes high quality programs in the core disciplines of syntax and phonology and in areas such as discourse, semantics, sociolinguistics, phonetics, historical linguistics and other areas of cognitive science.

Although the interests of faculty members and students cover practically the entire field of linguistics, the graduate group functions as a unit.

Faculty members and students work together on current research subjects at the Institute for Research in Cognitive Science, which fosters the development of science of the human mind via the interaction of researchers from the disciplines of linguistics, mathematical logic, psychology, philosophy, neuroscience and computer science.

Much of his research is classified under formal linguistics, natural language processing, artificial intelligence or cognitive science.

His research interests include statistical natural language processing, human-robot communication as well as cognitively plausible models for automatic acquisition of linguistic Structure.

Ani Nenkova, is an Associate Professor of Computer and Information Science, her main research areas are computational linguistics and artificial intelligence, with an emphasis on creating computational methods for analysis of text quality and style, discourse, affect recognition and summarization.

(Ithaca, New York) The Computational Linguistics Lab, a research and educational lab in the Department of Linguistics and the Faculty of Computing and Information, is part of the Natural Language Processing Group which includes faculty members and students in computer science, information science and psychology.

Methodologies used in the lab focus on statistical parsing of large data samples, including grammar development, parameter estimation as well as acquisition of lexical information from corpora.

Her goal is creating algorithms and systems which substantially improve a person’s ability to find, absorb, and extract information from on-line text.

She is an ACL Fellow for foundational contributions to co-reference resolution, information and opinion extraction, and to machine learning methods in natural language processing.

Hale has an interest in explaining the unique language-using abilities of the mind in terms of specific algorithms, computer architectures and data structures.

He has worked in the areas of symbolic/probabilistic models of syntax and the lexicon, on contrastive intonation, and on related phenomena including ellipsis and presupposition.

The department has an interest in building computational tools for the documentation of underdescribed languages, grammar engineering and natural language processing in general.

Selected faculty members: Emily Bender, a professor in the Department of Linguistics, and an Adjunct Professor in the Department of Computer Science and Engineering has research interests in multilingual grammar engineering, the study of variation, both within and across languages, and deep linguistic processing.

The group is involved in projects related to statistical machine translation, ontologies, summarization, question answering, natural language generation and information retrieval.

Selected faculty members: Kevin Knight, Professor in the Department of Computer Science, Director of the Natural Language Technologies and Fellow, Information Science Institute, has research interests in artificial intelligence, natural language processing, machine translation, machine learning, automata theory, and decipherment.

Knight is an ACL Fellow for significant contributions to statistical machine translation, automata for natural language processing as well as decipherment of historical manuscripts.

Marcu has research interests in large-scale natural language processing, including discourse processing, education, language generation, machine learning, machine translation, semantics and text summarization.

Ross has served on the editorial board of several of the major journals in his research areas and serves as a senior program committee member and area chair in major conferences in his research areas.

Richard Sproat, Adjunct Professor in the Linguistics Department is an ACL Fellow for significant contributions to computational morphology, text-to-speech synthesis, text normalization, Chinese language processing as well as computational approaches to writing systems.

(Berkeley, California) The Berkeley Natural Language Processing Group, part of the UC Berkeley Computer Science division, combines computer science, statistics and linguistics to create systems which cope with the richness and subtlety of the human language.

The group works in the areas of linguistic analysis, machine translation, computational linguistics, grounded semantics and unsupervised learning.

Selected faculty members: Daniel Klein Professor in the Computer Science Division has research interests in artificial intelligence, natural language processing, computational linguistics, and machine learning.

Berwick and his group investigate computation and cognition, including, language processing, computational models of language processing, and language change within the context of machine learning, modern grammatical theory as well as mathematical models of dynamical systems.

(Boulder, Colorado) The university’s Center for Computational Language and Education Research is involved in advancing human language technology and applying it to personalized learning for broad and diverse populations with varying language backgrounds and cognitive profiles.

He has research interests in how languages convey meaning to humans and computers, including how humans and computers process metaphors and other types of non-literal language.

Members of the Department of Linguistics work with members of the Computational Linguistics and Information Processing Laboratory to make advancements in areas including automatic summarization, machine translation, question answering, information retrieval and computational social science.

Resnick performs research in computational linguistics with interests in the application of natural language processing techniques to practical problems including machine translation and sentiment analysis as well as in the modeling of human linguistic processes.

His core research interests include formal and computational syntax, machine learning for natural language processing, and geotemporal grounding of natural language.

Students have opportunities to work on research projects with faculty members from other departments through independent study projects, classes or through combining faculty members from several departments for the thesis committee.

The computational linguistics group works on subjects including natural language generation , computational psycholinguistics, spoken dialogue systems, incremental parsing and interpretation, lexical and phonetic acquisition, discourse structure, computational pragmatics, and speech synthesis.

Selected faculty members: Michael White, Associate Professor in Department of Linguistics has research interests in natural language generation, paraphrasing and spoken language dialogue systems, grammar induction, monolingual word alignment and answer matching in dialogue.

Her research focuses on developing computational linguistics methods which show what language conveys beyond the literal meaning of words.

The faculty members have combined expertise in formal, behavioral, computational and imaging approaches to understanding the structure, processing, production, and acquisition of natural language.

Faculty members, postdoctoral fellows, and graduate students focus on various aspects of natural language structure and processing, and computational algorithms to describe these structures and to implement these processing mechanisms.

His research involves defining computational models of intelligent collaborative and conversational agents which can effectively interact with humans in a broad range of problem solving and analysis tasks.

He has produced papers in the areas of information retrieval, graph models of the Web, text summarization, machine translation, question answering, text generation, and information extraction.

Selected faculty members: John Lafferty, Lois Block Professor Department of Statistics, Department of Computer Science, and the College at the University of Chicago, has an interest in advancing applications in text and natural language processing, information retrieval, and other areas of language technologies.

His research areas include statistical machine learning, text and natural language processing, information retrieval, information theory and computational group theory.

The main interest of the project is unsupervised learning of natural language with a main focus on morphology, but the members of the project are involved in other areas.

Faculty members and students preform research in several areas of natural language processing such as machine translation, information extraction, text analysis and evaluation, natural language generation, psychocomputational modeling of language understanding, and speech recognition and analysis.

In particular Chodorow is interested in methods for detecting grammar and word usage errors which are common in the writing of non-native English speakers as well as problems of text coherence which are common in native and non-native writing.

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