PPT-SI485i : NLP

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 . Set 3. Language Models. 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.. Why. 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 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 . Semantic Role Labeling. Syntactic Variation. Last week, Min broke the window with a hammer.. The window was broken with a hammer by Min last week. With a hammer, Min broke the window last week. Last week, the window was broken by Min with a hammer. Outline. Some Sample NLP Task . [Noah Smith]. Structured Prediction For NLP. Structured Prediction Methods. Conditional Random Fields. Structured . Perceptron. Discussion. Motivating Structured-Output Prediction for NLP. Subproblems. . Meliha. . Yetisgen-Yildiz. From last week’s discussion. Presentation. Schedule. : . http. ://faculty.washington.edu/melihay/. MEBI591C.htm. 50 . minutes . presentation+discussion+question. 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 . Who wrote which Federalist papers?. 1787-8: anonymous essays try to convince New York to ratify U.S Constitution: . . Jay, Madison, Hamilton. . Authorship of 12 of the letters in dispute. 1963: solved by . 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. 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. Biomedical Informatics. UC San Diego. October 13, 2016. Chunnan Hsu. Ramana. . Seerapu. Scott Duvall. Olga Patterson. Hua Xu. Michael Matheny. Glenn . Gobbel. Tsung. -Ting . Kuo. Current members. The NLP working group is tasked to accurately extract phenotypes for three clinical conditions: Kawasaki Disease (KD), Weight Management / Obesity (WM/O), and Congestive Heart Failure (CHF), from tens of millions of clinical notes shared by participating institutes in . 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 .