Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others.
Natural language processing (NLP) is an interdisciplinary subfield of computer science and artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics. Typically data is collected in text corpora, using either rule-based, statistical or neural-based approaches in machine learning and deep learning.
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. NLU has been considered an AI-hard problem.
The Association for Computational Linguistics (ACL) is a scientific and professional organization for people working on natural language processing. Its namesake conference is one of the primary high impact conferences for natural language processing research, along with EMNLP. The conference is held each summer in locations where significant computational linguistics research is carried out.
A symbolic linguistic representation is a representation of an utterance that uses symbols to represent linguistic information about the utterance, such as information about phonetics, phonology, morphology, syntax, or semantics. Symbolic linguistic representations are different from non-symbolic representations, such as recordings, because they use symbols to represent linguistic information rather than measurements.
Shallow parsing is an analysis of a sentence which first identifies constituent parts of sentences and then links them to higher order units that have discrete grammatical meanings. While the most elementary chunking algorithms simply link constituent parts on the basis of elementary search patterns, approaches that use machine learning techniques can take contextual information into account and thus compose chunks in such a way that they better reflect the semantic relations between the basic constituents. That is, these more advanced methods get around the problem that combinations of elementary constituents can have different higher level meanings depending on the context of the sentence.
A language model is a probabilistic model of a natural language. In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.
Charles J. Fillmore was an American linguist and Professor of Linguistics at the University of California, Berkeley. He received his Ph.D. in Linguistics from the University of Michigan in 1961. Fillmore spent ten years at Ohio State University and a year as a Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University before joining Berkeley's Department of Linguistics in 1971. Fillmore was extremely influential in the areas of syntax and lexical semantics.
FrameNet is a group of online lexical databases based upon the theory of meaning known as Frame semantics, developed by linguist Charles J. Fillmore. The project's fundamental notion is simple: most words' meanings may be best understood in terms of a semantic frame, which is a description of a certain kind of event, connection, or item and its actors.
In linguistics, a treebank is a parsed text corpus that annotates syntactic or semantic sentence structure. The construction of parsed corpora in the early 1990s revolutionized computational linguistics, which benefitted from large-scale empirical data.
Frederick Jelinek was a Czech-American researcher in information theory, automatic speech recognition, and natural language processing. He is well known for his oft-quoted statement, "Every time I fire a linguist, the performance of the speech recognizer goes up".
ARPABET is a set of phonetic transcription codes developed by Advanced Research Projects Agency (ARPA) as a part of their Speech Understanding Research project in the 1970s. It represents phonemes and allophones of General American English with distinct sequences of ASCII characters. Two systems, one representing each segment with one character and the other with one or two (case-insensitive), were devised, the latter being far more widely adopted.
Yorick Alexander Wilks FBCS was a British computer scientist. He was an emeritus professor of artificial intelligence at the University of Sheffield, visiting professor of artificial intelligence at Gresham College, senior research fellow at the Oxford Internet Institute, senior scientist at the Florida Institute for Human and Machine Cognition, and a member of the Epiphany Philosophers.
In natural language processing, semantic role labeling is the process that assigns labels to words or phrases in a sentence that indicates their semantic role in the sentence, such as that of an agent, goal, or result.
Linguistics is the scientific study of language. Linguistics is based on a theoretical as well as a descriptive study of language and is also interlinked with the applied fields of language studies and language learning, which entails the study of specific languages. Before the 20th century, linguistics evolved in conjunction with literary study and did not employ scientific methods. Modern-day linguistics falls under the behavioral sciences because it entails a comprehensive, systematic, objective, and precise analysis of all aspects of language – i.e., the cognitive, the social, the cultural, the psychological, the environmental, the biological, the literary, the grammatical, the paleographical, and the structural.
The following outline is provided as an overview of and topical guide to natural-language processing:
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.
Mari Ostendorf is a professor of electrical engineering in the area of speech and language technology and the vice provost for research at the University of Washington.
Ruslan Mitkov is a professor at Lancaster University, and a researcher in Natural Language Processing and Computational Linguistics. He completed his PhD at Technical University of Dresden under the supervision of Nikolaus Joachim Lehmann. He has published more than 240 refereed papers and is best known for his contributions to Anaphora Resolution, and his seminal work in computer-aided generation of multiple-choice tests among others.
Martha (Stone) Palmer is an American computer scientist. She is best known for her work on verb semantics, and for the creation of ontological resources such as PropBank and VerbNet.
