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

Corpora and Statistical Methods Lecture 6 Semantic similarity Part 1 Synonymy Different phonological orthographic words highly related meanings sofa couch boy lad Traditional definition. 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”?. . Multi-label Protein Subcellular Localization. Shibiao WAN and Man-Wai MAK. The Hong Kong Polytechnic University. Sun-Yuan KUNG. Princeton University. Outline. Introduction and Motivation. Retrieval of GO Terms. medical dictations. Stefan Petrik . , . Christina . Drexel, . Leo Fessler . , . Jeremy Jancsary . , . Alexandra Klein . ,Gernot . Kubin . , . Johannes Matiasek . , . Franz Pernkopf . , . Harald . Trost. １２月７日. 研究会. 祭都援炉. (. マットエンロ. ). Up until now: Getting to know NLP. “Speech and Language Processing” (. Jurafsky. & Martin). 論文：. On-Demand Information Extract . 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?. Drishti. . Wali. (13266). Nirbhay. . Modhe. (13444). Word Sense Disambiguation . The task of automatically assigning a sense to an . ambiguous word according . to the context in which it is present.. Word . Similarity: . Distributional Similarity (I). Problems with thesaurus-based . meaning. We don’t have a thesaurus for every language. Even if we do, . they have problems with . recall. M. any . 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. 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. . 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”?. Ladislav Gallay. Supervisor. : Ing. Marián Šimko, PhD.. Slovak University of Technology. Faculty of Informatics and Information Technologies . Lemmatization. basic form of a word. : . houses . > . Quiz. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 3. Deer-mouse. 4. Deer-roof. Quiz Answer. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 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. .