PPT-SI485i : NLP

Set 6 Sentiment and Opinions Its about finding out what people think Can be big business Someone who wants to buy a camera Looks for reviews online Someone who just bought a camera Writes reviews online. Set 10. Lexical Relations. s. lides adapted from Dan . Jurafsky. and Bill . MacCartney. Three. levels of . m. eaning. Lexical . Semantics (words). Sentential . / Compositional / Formal Semantics. Discourse . John Blitzer. 自然语言计算组. http://research.microsoft.com/asia/group/nlc/. Why should I know about machine learning? . This is an NLP summer school. Why should I care about machine learning?. SI485i : NLP Set 3 Fall 2012 : Chambers Language Modeling • Which sentence is most likely (most probable)? I saw this dog running across the street. Saw dog this I running across street the. Wh By Cindy Carless. 14 Nov 14. What a mouthful…. So much in fact that I thought I had bitten off more than I could chew…. Yet for “Tasting” . LetsTest. it seems very appropriate. Some appetisers to focus on. Detecting Questions in Micro-text. Kyle Dent <. kdent@parc.com. >. Original Question: Do people use Twitter to ask questions?. First Answer: Of course.. But to what extent and what kinds of questions?. Morphology and the Lexicon. Mental Lexicon. What is the meaning of cat? Its pronunciation? Part of speech?. What is the meaning of . wug. ?. What is the meaning of . cluvious. ?. Compare . traftful. and . the almighty……….. . NLP. . DR.Eman. . Diab. introduction. introduction. The word . Neuro. linguistic programming can be broken down to three distinct words: . Subproblems. . Meliha. . Yetisgen-Yildiz. From last week’s discussion. Presentation. Schedule. : . http. ://faculty.washington.edu/melihay/. MEBI591C.htm. 50 . minutes . presentation+discussion+question. Regina Barzilay. What is NLP?. Goal: intelligent processing of human language. Not just effective string matching. Applications of NLP technology:. Less ambitious (but practical goals): spelling corrections, name entity extraction. Properties of Propositional Logic. Pros. Compositional. Declarative. Cons. Limited expressive power. Represents facts. First Order Logic. Used to represent. Objects – Martin the cat. Relations – Martin and Moses are brothers. Parser. Earley. parser. Problems with left recursion in top-down parsing. VP . . VP PP. Background. Developed by Jay Earley in 1970. No need to convert the grammar to CNF. Left to right. Complexity. s. lides adapted from Dan . Jurafsky. and Bill . MacCartney. Three. levels of . m. eaning. Lexical . Semantics (words). Sentential / Compositional / Formal Semantics. Discourse . or Pragmatics. m. eaning + context + world knowledge. Dan Jurafsky. Stanford University. Spring 2020 . Introduction and Course Overview. Thanks to . Tsvetkov. and Black course . for ideas and slides!. How should we use NLP for good and not for bad?. The common misconception is that language has to do with . by Hua Xu. Recent Activities. ETL tool development. Note Type Normalization. COVID-19 lab test normalization . A Potential ETL Workflow for NLP. Note. Note_NLP. Measure-. ment. Condition. Procedure. Drug.