PDF-Recursive Deep Models for Discourse Parsing Jiwei Li

R China Language Technology Institute Carnegie Mellon University Pittsburgh PA 15213 USA jiweilstanfordedu alicerumengfoxmailcom ehovyandrewcmuedu Abstract Textlevel discourse parsing remains a chal lenge most approaches employ features that fail to. CS 4705. Julia Hirschberg. 1. Some slides adapted from Kathy McKeown and Dan Jurafsky. Syntactic Parsing. Declarative . formalisms like CFGs, FSAs define the . legal strings of a language. -- but only tell you whether a given string is legal in a particular language. Lana Lazebnik. UNC Chapel Hill. sky. sidewalk. building. road. car. person. car. mountain. The past: . “closed universe. ” datasets. Tens of classes, hundreds of images, offline learning. He et al. (2004), . Julia Hirschberg. CS 4705. Thanks to Dan Jurafsky, Diane Litman, Andy Kehler, Jim Martin . What makes a text or dialogue coherent? . “Consider, for example, the difference between passages (18.71) and (18.72). Almost certainly not. The reason is that these utterances, when juxtaposed, will not exhibit coherence. Do you have a discourse? Assume that you have collected an arbitrary set of well-formed and independently interpretable utterances, for instance, by randomly selecting one sentence from each of the previous chapters of this book.” . Introducing. A. Knowledge-Based. Conversational Agent. Bill DeSmedt. Amber Productions. Natural Language Understanding. The Test according to Turing. Natural Language Understanding. The Test according to Turing. Pascal Denis. ALPAGE Project Team. INRIA . Rocquencourt. F-78153 Le . Chesnay. , France. Jason . Baldridge. Department of Linguistics. University of Texas at Austin. In Proceedings of EMNLP-2008. 1. Yi-Ting Huang. Prof. O. . Nierstrasz. Thanks to Jens Palsberg and Tony Hosking for their kind permission to reuse and adapt the CS132 and CS502 lecture notes.. http://www.cs.ucla.edu/~palsberg/. http://www.cs.purdue.edu/homes/hosking/. David Kauchak. CS159 – Spring 2011. some slides adapted from Ray Mooney. Admin. Updated slides/examples on . backoff. with absolute discounting (I’ll review them again here today). Assignment 2. Top-down vs. bottom-up parsing. Top-down . vs. bottom-up . parsing. Ex. Ex. Ex. Ex. +. Nat. *. Nat. Nat. Ex. Ex. . . . Nat. | . (. Ex. ). | . Ex. . +. . Ex. | . Ex. . *. . Ex. Matched input string. Richard . Socher. . Cliff . Chiung. -Yu Lin . Andrew Y. Ng . Christopher D. Manning . Slides. . &. . Speech:. . Rui. . Zhang. Outline. Motivation. . &. . Contribution. Recursive. . Neural. Niranjan Balasubramanian. March 24. th. 2016. Credits: . Many slides from:. Michael Collins, . Mausam. , Chris Manning, . COLNG 2014 Dependency Parsing Tutorial, . Ryan McDonald, . . Joakim. . Nivre. Some slides are based on:. PPT presentation on dependency parsing by . Prashanth. . Mannem. Seven Lectures on Statistical . Parsing by Christopher Manning. . Constituency parsing. Breaks sentence into constituents (phrases), which are then broken into smaller constituents. CS2110 – . Fall 2013. 1. Pointers to the textbook. 2. Parse trees. : . Text p. age . 592 (23.34), Figure 23-31. Definition of . Java . Language, sometimes useful: . http://docs.oracle.com/javase/specs/jls/se7/html/index.html. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand