PPT-Various Improvements in Vector Space Word Representations

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 . In this paper we ex amine the vectorspace word representations that are implicitly learned by the inputlayer weights We 64257nd that these representations are surprisingly good at capturing syntactic and semantic regularities in language and that ea 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”?. Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. Natural Language Processing. Tomas Mikolov, Facebook. ML Prague 2016. Structure of this talk. Motivation. Word2vec. Architecture. Evaluation. Examples. Discussion. Motivation. Representation of text is very important for performance of many real-world applications: search, ads recommendation, ranking, spam filtering, …. Chao Xing. CSLT Tsinghua. Why?. Chris Dyer. group had gotten a lot of brilliant achievements in 2015, and their research interest match to ours. . And in some area, we two groups almost think same way, but we didn’t do so well as they did.. & 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. . By Cass Sherman. For Fulfillment of the Ph.D. Defense Requirement. Introduction: . Littlewood. -Richardson Numbers. For dominant weights . , . , …, . of . , one can consider the L-R number below.. and their Compositionality. Presenter: Haotian Xu. Roadmap. Overview. The Skip-gram Model with Different . Objective Functions. Subsampling of Frequent Words. Learning Phrases. CNN for Text Classification. 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. 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. Bravais lattice, real lattice vector . R. , reciprocal lattice vector . K. , point group, space group, group representations, Bloch theorem. Discrete lattices. 1D. 2D. 3D. a. Bravais lattice: each unit cell has only one atom (5 types in 2D). 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. .