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Statistical Parsing
Statistical parsing is a group of parsing methods within natural language processing. The methods have in common that they associate grammar rules with a probability. Grammar rules are traditionally viewed in computational linguistics as defining the valid sentences in a language. Within this mindset, the idea of associating each rule with a probability then provides the relative frequency of any given grammar rule and, by deduction, the probability of a complete parse for a sentence. (The probability associated with a grammar rule may be induced, but the application of that grammar rule within a parse tree and the computation of the probability of the parse tree based on its component rules is a form of deduction.) Using this concept, statistical parsers make use of a procedure to search over a space of all candidate parses, and the computation of each candidate's probability, to derive the most probable parse of a sentence. The Viterbi algorithm is one popular method of search ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parsing
Parsing, syntax analysis, or syntactic analysis is the process of analyzing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar. The term ''parsing'' comes from Latin ''pars'' (''orationis''), meaning part (of speech). The term has slightly different meanings in different branches of linguistics and computer science. Traditional sentence parsing is often performed as a method of understanding the exact meaning of a sentence or word, sometimes with the aid of devices such as sentence diagrams. It usually emphasizes the importance of grammatical divisions such as subject and predicate. Within computational linguistics the term is used to refer to the formal analysis by a computer of a sentence or other string of words into its constituents, resulting in a parse tree showing their syntactic relation to each other, which may also contain semantic and other information (p-values). Some parsing ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Eugene Charniak
Eugene Charniak is a professor of computer Science and cognitive Science at Brown University. He holds an A.B. in Physics from the University of Chicago and a Ph.D. from M.I.T. in Computer Science. His research has always been in the area of language understanding or technologies which relate to it, such as knowledge representation, reasoning under uncertainty, and learning. Since the early 1990s he has been interested in statistical techniques for language understanding. His research in this area has included work in the subareas of part-of-speech tagging, probabilistic context-free grammar induction, and, more recently, syntactic disambiguation through word statistics, efficient syntactic parsing, and lexical resource acquisition through statistical means. He is a Fellow of the American Association of Artificial Intelligence and was previously a Councilor of the organization. He was also honored with the 2011 Association for Computational Linguistics Lifetime Achievement ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stochastic Context-free Grammar
Grammar theory to model symbol strings originated from work in computational linguistics aiming to understand the structure of natural languages. Probabilistic context free grammars (PCFGs) have been applied in probabilistic modeling of RNA structures almost 40 years after they were introduced in computational linguistics. PCFGs extend context-free grammars similar to how hidden Markov models extend regular grammars. Each production is assigned a probability. The probability of a derivation (parse) is the product of the probabilities of the productions used in that derivation. These probabilities can be viewed as parameters of the model, and for large problems it is convenient to learn these parameters via machine learning. A probabilistic grammar's validity is constrained by context of its training dataset. PCFGs have application in areas as diverse as natural language processing to the study the structure of RNA molecules and design of programming languages. Designing effic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Semantics
In linguistics, statistical semantics applies the methods of statistics to the problem of determining the meaning of words or phrases, ideally through unsupervised learning, to a degree of precision at least sufficient for the purpose of information retrieval. History The term ''statistical semantics'' was first used by Warren Weaver in his well-known paper on machine translation. He argued that word sense disambiguation for machine translation should be based on the co-occurrence frequency of the context words near a given target word. The underlying assumption that "a word is characterized by the company it keeps" was advocated by J.R. Firth. This assumption is known in linguistics as the distributional hypothesis. Emile Delavenay defined ''statistical semantics'' as the "statistical study of meanings of words and their frequency and order of recurrence". "Furnas et al. 1983" is frequently cited as a foundational contribution to statistical semantics. An early success in the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Machine Translation
Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation, and has more recently been superseded by neural machine translation in many applications (see this article's final section). The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory. Statistical machine translation was re-introduced in the late 1980s and early 1990s by researchers at IBM's Thomas J. Watson Research Center and has contributed to the significant resurgence in interest in machine translation in recent years. Before the introduction of neural machine translation, it was by far the most widely studied machine transl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Joshua Goodman
Joshua () or Yehoshua ( ''Yəhōšuaʿ'', Tiberian: ''Yŏhōšuaʿ,'' lit. 'Yahweh is salvation') ''Yēšūaʿ''; syr, ܝܫܘܥ ܒܪ ܢܘܢ ''Yəšūʿ bar Nōn''; el, Ἰησοῦς, ar , يُوشَعُ ٱبْنُ نُونٍ '' Yūšaʿ ibn Nūn''; la, Iosue functioned as Moses' assistant in the books of Exodus and Numbers, and later succeeded Moses as leader of the Israelite tribes in the Hebrew Bible's Book of Joshua. His name was Hoshea ( ''Hōšēaʿ'', lit. 'Save') the son of Nun, of the tribe of Ephraim, but Moses called him "Yehoshua" (translated as "Joshua" in English),''Bible'' the name by which he is commonly known in English. According to the Bible, he was born in Egypt prior to the Exodus. The Hebrew Bible identifies Joshua as one of the twelve spies of Israel sent by Moses to explore the land of Canaan. In Numbers 13:1, and after the death of Moses, he led the Israelite tribes in the conquest of Canaan, and allocated lands to the tribes. According to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Michael Collins (computational Linguist)
Michael J. Collins (born 4 March 1970) is a researcher in the field of computational linguistics. He is the Vikram S. Pandit Professor of Computer Science at Columbia University. His research interests are in natural language processing as well as machine learning and he has made important contributions in statistical parsing and in statistical machine learning. In his studies Collins covers a wide range of topics such as parse re-ranking, tree kernels, semi-supervised learning, machine translation and exponentiated gradient algorithms with a general focus on discriminative models and structured prediction. One notable contribution is a state-of-the-art parser for the Penn Wall Street Journal corpus. As of 11 November 2015, his works have been cited 16,020 times, and he has an h-index of 47. Collins worked as a researcher at AT&T Labs between January 1999 and November 2002, and later held the positions of assistant and associate professor at M.I.T. Since January 2011, he ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Entropy Maximization
The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge about a system is the one with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information). Another way of stating this: Take precisely stated prior data or testable information about a probability distribution function. Consider the set of all trial probability distributions that would encode the prior data. According to this principle, the distribution with maximal information entropy is the best choice. History The principle was first expounded by E. T. Jaynes in two papers in 1957 where he emphasized a natural correspondence between statistical mechanics and information theory. In particular, Jaynes offered a new and very general rationale why the Gibbsian method of statistical mechanics works. He argued that the entropy of statistical mechanics and the information entropy of informatio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
James Curran (linguist)
James R. Curran is a computational linguist and senior lecturer at the University of Sydney. He holds a PhD in Informatics from the University of Edinburgh. Research Curran's research focuses on natural language processing, making him one of the few Australian computational linguists. Specifically Curran's research has focused on the area of natural language processing known as combinatory categorial grammar parsing. In addition to his contributions to NLP, Curran has produced a paper on the development of search engines to assist in driving problem based learning. Within NLP, he has published papers on combinatory categorial grammar parsing as well as question answering systems. Works Curran has co-authored software packages such as C&C tools, a CCG parser (with Stephen Clark). Educational work In addition to his work as a University of Sydney lecturer, Curran directs the National Computer Science School The National Computer Science School (NCSS) is an annual comput ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
David Magerman
David Mitchell Magerman (born 1968) is an American computer scientist and philanthropist. He spent 22 years working for an investment management company and hedge fund, Renaissance Technologies. Early life and education Magerman was born to Melvin and Sheila Magerman. His father owned All-City Taxi in Miami, Florida, and his mother was a secretary for a group of accounting firms in Tamarac. Magerman received his Ph.D. degree from Stanford University in computer science. He also received his B.S. from the University of Pennsylvania. Career Magerman spent two decades working for James Simons’s New York-based investment management company Renaissance Technologies, where he developed trading algorithms. In 2017, Magerman publicly opposed the views of his boss, Robert Mercer, concerning politics and race issues in America. Mercer, the co-CEO of Renaissance Technology, suspended Magerman without pay and later made the suspension permanent. That same year Magerman filed a federal ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fred Jelinek
Frederick Jelinek (18 November 1932 – 14 September 2010) 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". Jelinek was born in Czechoslovakia before World War II and emigrated with his family to the United States in the early years of the communist regime. He studied engineering at the Massachusetts Institute of Technology and taught for 10 years at Cornell University before accepting a job at IBM Research. In 1961, he married Czech screenwriter Milena Jelinek. At IBM, his team advanced approaches to computer speech recognition and machine translation. After IBM, he went to head the Center for Language and Speech Processing at Johns Hopkins University for 17 years, where he was still working on the day he died. Personal life Jelinek was born on November 18, 1932, as Bedřich ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
