PPT-NLP Introduction to NLP Word Sense Disambiguation

Introduction Polysemy Words have multiple senses Example Lets have a drink in the bar I have to study for the bar Bring me a chocolate bar Homonymy May I come in Lets meet again in May. 05329017 Under the guidance of Prof Om Damani Kanwal Rekhi School of Information Technology Kanwal Rekhi School of Information Technology Indian Institute of Technology Powai Mumbai 20062007 brPage 2br Abstract Word sense disambiguation WSD is the t upenn edu Abstract This paper presents an unsupervised learn ing algorithm for sense disambiguation that when trained on unannotated English text rivals the performance of supervised techniques that require timeconsuming hand annotations The algorit 1 WSD MAS.S60. Catherine . Havasi. Rob Speer. Banks?. The edge of a river. “I fished on the bank of the Mississippi.”. A financial institution. “Bank of America failed to return my call.”. The building that houses the financial institution. Julia Hirschberg. CS 4705. Slides adapted from Kathy McKeown, Dan Jurafsky, Jim Martin and Chris Manning. Lexical Semantics. The meanings of . individual words. Formal Semantics. (or Compositional Semantics or Sentential Semantics). A Unified Approach for Measuring Semantic Similarity. Mohammad . Taher. . Pilehvar. David . Jurgens. Roberto Navigli. Semantic. . Similarity. ; . how. . similar. are a . pair. of . lexical. . items. ThisworkwaspartiallyfundedbytheInteropNoE(508011)6thEUFP.Author quite a number of other factors in addition to 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.. 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. Aim to get back on Tuesday. I grade on a curve. One for graduate students. One for undergraduate students. Comments?. Midterm. You should have received email with your grade – if not, let . Madhav. Slides adapted from Dan Jurafsky, Jim Martin and Chris Manning. Next week. Finish semantics. Begin machine learning for NLP. Review for midterm. Midterm. October 27. th. Will cover everything through semantics. Dan Jurafsky. Stanford University. Lecture 2: Word Sense Disambiguation. Word Sense Disambiguation (WSD). Given . A. . word in . context . A fixed inventory of potential word . senses. Decide which sense of the word this . Slides adapted from Dan Jurafsky, Jim Martin and Chris Manning. This week. Finish semantics. Begin machine learning for NLP. Review for midterm. Midterm. October . 27. th, . Where: 1024 . Mudd. (here).