PPT-Measuring Word Relatedness Using Heterogeneous Vector Space

Author : faustina-dinatale | Published Date : 2015-11-20

Scott Wentau Yih Microsoft Research Joint work with Vahed Qazvinian University of Michigan Measuring Semantic Word Relatedness How related are words movie and

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Measuring Word Relatedness Using Heterogeneous Vector Space: Transcript

Scott Wentau Yih Microsoft Research Joint work with Vahed Qazvinian University of Michigan Measuring Semantic Word Relatedness How related are words movie and popcorn. 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 . Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. Manaal Faruqui. Sujay. . Jauhar. , Jesse Dodge. Chris Dyer, Noah Smith. Distributional Semantics. “You shall know a word by the company it keeps”. (Harris 1954; Firth, 1957). …I will take what is mine with . Daniel Svozil. based on excelent video lectures by Gilbert Strang, MIT. http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/VideoLectures/index.htm. Lectur. e. 5, Lecture 6. Transposes. How to write tra. & Subspaces. Kristi Schmit. Definitions. A subset W of vector space V is called a . subspace . of V . iff. The. . zero vector of V is in W.. W. is closed under vector addition, for each . u. . . Annotators. “. Diego Maradona won against Mexico. ”. Dictionary of terms. against. Diego. Maradona. Mexico. won. 2.2. 5.1. 9.1. 1.0. 0.1. Term Vector. Similarity(. v,w. ) ≈ . cos(. a. ). . t. 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. 4.1 Vectors in . R. n. 4.2 Vector Spaces. 4.3 Subspaces of Vector Spaces. 4.4 Spanning Sets and Linear Independence. 4.5 Basis and Dimension. 4.6 Rank of a Matrix and Systems of Linear Equations. 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”?. . H. HABEEB RANI. Assistant professor of Mathematics. Department of mathematics. Hajee. . Karutha. . Rowther. . Howdia. College. VECTOR SPACES. Definition. Examples. THEOREM. Subspaces. 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. . . on Microblogs. Hongzhao Huang. huangh9@rpi.edu. Advisor: Dr. Heng Ji. Computer Science Department. Rensselaer Polytechnic Institute. April 9, 2015. Doctoral Committee: . Dr. Heng Ji (. Chair, RPI).

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