PPT-Semantic similarity, vector space models and word-sense dis

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. Scott Wen-tau . Yih. Joint work with. . Geoffrey Zweig & . John . Platt. Microsoft Research. A vector space model that can distinguish Antonyms from Synonyms!. Vector Space Model. Text . objects . A Unified Approach for Measuring Semantic Similarity. Mohammad . Taher. . Pilehvar. David . Jurgens. Roberto Navigli. Semantic. . Similarity. ; . how. . similar. are a . pair. of . lexical. . items. Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. WordNet. Lubomir. . Stanchev. Example . Similarity Graph. Dog. Cat. 0.3. 0.3. Animal. 0.8. 0.2. 0.8. 0.2. Applications. If we type . automobile. . in our favorite Internet search engine, for example Google or Bing, then all top results will contain the word . Focus on . word and sentence . similarity. Formal side: define similarity in principle. Characterizing word meaning . in context. Given a word in a particular sentence context: Can we characterize its meaning without reference to dictionary senses?. Instructor: Paul Tarau, based on . Rada. . Mihalcea’s. original slides. Note. : Some of the material in this slide set was adapted from a tutorial given by . Rada. . Mihalcea. & Ted Pedersen at ACL 2005. 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. Dan Jurafsky. Stanford University. Introduction and Course Overview. What is Computational Lexical Semantics. Any computational process involving word meaning!. Computing . Word Similarity . Distributional (Vector) Models of . 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. 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 . Scott Wen-tau Yih . (Microsoft Research). Joint work with. . Vahed Qazvinian . (University of . Michigan). Measuring Semantic Word Relatedness. How related are words “movie” and “popcorn”?. Xiangen . Hu. CCNU & . UoM. Agenda. Introduction. Basic semantic comparison techniques. Examples . of semantic spaces. A general framework. A few . applications. Hands-. on . （. if time permits). Why vector models of meaning?. computing the similarity between words. “. fast. ” is similar to “. rapid. ”. “. tall. ” is similar to “. height. ”. Question answering:. Q. : “. How . 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. .