PPT-SI485i : NLP

Author : celsa-spraggs | Published Date : 2015-10-04

Set 8 PCFGs and the CKY Algorithm PCFGs We saw how CFGs can model English sort of Probabilistic CFGs put weights on the production rules NP gt DET NN with probability

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SI485i : NLP: Transcript

Set 8 PCFGs and the CKY Algorithm PCFGs We saw how CFGs can model English sort of Probabilistic CFGs put weights on the production rules NP gt DET NN with probability 034 NP gt . Set 6. Sentiment and Opinions. It's 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?. 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. 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. In the Context of the ETC Project. SAW Intro. Wiki. Users collaboratively develop and organize content. Information Analysis is up to the user. Goal: . “Self-aware wiki that can develop and organize its content. alebo prečo . som mal dávať väčší pozor na . slovenčine. Róbert . Móro. 26. .. 2. .201. 3. moro. @. fiit. .. stuba.sk. I made her duck.. I made her duck.. Uvaril (upiekol) som jej kačku.. Uvaril som kačku, ktorá jej patrí.. Language Annotation, Modeling, and Processing . Toolkit (CLAMP) . Hua Xu. School of Biomedical Informatics, . University . of Texas Health Science Center at Houston . 1. The Transportability Problem of . 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. Text Similarity. Motivation. People can express the same concept (or related concepts) in many different ways. For example, “the plane leaves at 12pm” vs “the flight departs at noon”. Text similarity is a key component of Natural Language Processing. Jiho . Han. Ronny (. Dowon. ) . Ko. Objective:. automatically generate the summary of review extracting the strength/weakness of the product. Use NLP techniques to predict ratings. Similar to sentimental analysis. 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.

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