PPT-Do Supervised Distributional Methods Really Learn Lexical Inference Relations?

Omer Levy Ido Dagan Bar Ilan University Israel Steffen Remus Chris Biemann Technische Universität Darmstadt Germany Lexical Inference Lexical Inference Task Definition. 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.” . Kari Lock Morgan. Department of Statistical Science, Duke University. kari@stat.duke.edu. . with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock. ECOTS. 5/16/12. Hypothesis Testing:. Use a formula to calculate a test statistic. Vsevolod. . Kapatsinski. University of Oregon. Two kinds of change in Usage-based Phonology. (. Bybee. 1976, . 2001. , 2002), Phillips (1984, 2001). Articulatorily. -motivated sound change. Driven by . Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. SVEM21 . 3. Structuralist Semantics. Jordan Zlatev. 1. General characteristics. Semantic approaches can be:. Onomasilogical. . (from concept/domain to lexeme) vs. . semasiological. . (from lexeme to concept/meaning). Katrin Erk. University of Texas at . Austin. Meaning in Context Symposium. München. September 2015. Joint work with Gemma . Boleda. Semantic features by example: . Katz & Fodor. Different meanings of a word characterized by lists of semantic features. Kari Lock Morgan. Department of Statistical Science, Duke University. kari@stat.duke.edu. . with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock. ECOTS. 5/16/12. Hypothesis Testing:. Use a formula to calculate a test statistic. a Probabilistic . Lexical . Inference System. . Eyal Shnarch. ,. . Ido . Dagan, Jacob . Goldberger. PLIS - Probabilistic Lexical Inference System. 1. /34. The . entire talk in a single sentence. Harris T. Lin. , . Sanghack. Lee, . Ngot. Bui and . Vasant. . Honavar. Artificial Intelligence Research Laboratory. Department of Computer Science. Iowa State University. htlin@iastate.edu. Introduction. On. e. . par. t. . o. f. . kn. o. win. g. . th. e. . meaning. s. . o. f. . l. e. x. eme. s. . i. n. . a. n. y. . languag. e. . is th. e. . recognitio. n. . tha. t. . tw. o. . o. r. . mor. Text-to-Scene Generation. 1. Bob Coyne (. coyne@cs.columbia.edu. ). Owen . Rambow. (. rambow@ccls.columbia.edu. ). . Richard . Sproat. (. rws@xoba.com. ). Julia Hirschberg (. julia@columbia.edu. ). Katrin . Erk. You can get an idea of what a word means from observing it in context. He filled the . wampimuk. , passed it around, and we all drank some. We found a little hairy . wampimuk. . sleeping behind a tree. . S. imilarity to Semantic Relations. Georgeta. . Bordea. , November 25. Based on a talk by Alessandro . Lenci. . titled “Will DS ever become Semantic?”, Jan 2014. Distributional Semantics . (DS. Bryan Rink. University of Texas at Dallas. December 13, 2013. Outline. Introduction. Supervised relation identification. Unsupervised relation discovery. Proposed work. Conclusions. Motivation. We think about our world in terms of:.