PPT-Detecting compositionality using semantic vector space models based on syntactic context

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Guillermo Garrido and Anselmo Peñas NLP amp IR Group at UNED Madrid Spain ggarridoanselmo lsiunedes Shared Task System Description ACLHLT 2011 Workshop

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Detecting compositionality using semantic vector space models based on syntactic context: Transcript

Guillermo Garrido and Anselmo Peñas NLP amp IR Group at UNED Madrid Spain ggarridoanselmo lsiunedes Shared Task System Description ACLHLT 2011 Workshop . Wu Jason Chuang Christopher D Manning Andrew Y Ng and Christopher Potts Stanford University Stanford CA 94305 USA richardsocherorg aperelygjcchuangang csstanfordedu jeaneismanningcgpotts stanfordedu Abstract Semantic word spaces have been very use f Systems & Resources. Ling573. NLP Systems & Applications. April 8, 2010. Roadmap. Two extremes in QA systems:. LCC’s PowerAnswer-2 . Insight’s Patterns…. Question classification (Li & Roth). Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. Corpora and Statistical Methods. Lecture 6. Word sense disambiguation. Part 2. What are word senses?. Cognitive definition: . mental representation of meaning . used in psychological experiments. relies on introspection (notoriously deceptive). Nikhil . Rasiwasia. , . Nuno. . Vasconcelos. Statistical Visual Computing Laboratory. University of California, San Diego. Thesis Defense. Ill pause for a few moments so that you all can finish reading this. . for concepts. Compute posterior probabilities . or . Semantic Multinomial . (SMN) under appearance models.. But, suffers from . contextual noise. Model the distribution of SMN for each concept. : assigns high probability to “. Lexical Semantics. 2. Information Retrieval System. IR. System. Query String. Document. corpus. Ranked. Documents. 1. Doc1. 2. Doc2. 3. Doc3. .. .. The Vector-Space Model. Graphic Representation. Lexical Semantics. 2. Information Retrieval System. IR. System. Query String. Document. corpus. Ranked. Documents. 1. Doc1. 2. Doc2. 3. Doc3. .. .. The Vector-Space Model. Graphic Representation. algorithms in. Question Answering. Alexander . Solovyev. Bauman Moscow Sate Technical University. a-soloviev@mail.ru. 20.10.2011. 1. RCDL. Voronezh.. Agenda. Question Answering and Answer Validation task. algorithms in. Question Answering. Alexander . Solovyev. Bauman Moscow Sate Technical University. a-soloviev@mail.ru. 20.10.2011. 1. RCDL. Voronezh.. Agenda. Question Answering and Answer Validation task. computing the similarity between words. “. fast. ” is similar to “. rapid. ”. “. tall. ” is similar to “. height. ”. Question answering:. Q. : “. How . tall. . is Mt. Everest?”. Candidate A: “The . UN SYSTEME SYNTAXIQUE ET SEMANT|QUE INTEGRE POUR LA COMPREHENSION DU LANGAGE NATUREL Fr&l~rique SEGOND(I) et Karen JENSEN(2) (l)Institut National des T616communications (Evry, France); email: segond the Department set of this grammar the grammar in which very powerful of movinga new AcknowledgmentsI am very grateful to the many people who have influenced this research and madethis thesis possible Many slides in this section are adapted from Prof. Joydeep Ghosh (UT ECE) who in turn adapted them from Prof. Dik Lee (Univ. of Science and Tech, Hong Kong). 1. These notes are based, in part, on notes by Dr. Raymond J. Mooney at the University of Texas at Austin. .

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