PPT-Knowledge and Tree-Edits in Learnable Entailment

Author : jane-oiler | Published Date : 2016-04-06

Proofs Asher Stern Amnon Lotan Shachar Mirkin Eyal Shnarch Lili Kotlerman Jonathan Berant and Ido Dagan TAC November 2011 NIST Gaithersburg Maryland

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Knowledge and Tree-Edits in Learnable Entailment: Transcript

Proofs Asher Stern Amnon Lotan Shachar Mirkin Eyal Shnarch Lili Kotlerman Jonathan Berant and Ido Dagan TAC November 2011 NIST Gaithersburg Maryland USA. Peter Clark. Allen Institute for Artificial Intelligence (AI2). Mission: . achieve scientific breakthroughs by constructing AI systems with reasoning, learning, and reading capabilities. . 2. Overall Goals. QA4MRE, . and Machine Reading. Peter Clark. Vulcan Inc.. What is . Machine Reading?. Not (just). parsing + word senses. Construction of a . coherent representation. of the scene the text describes. evolvability. Vitaly. Feldman . Almaden. Research Center. Learning from examples vs . evolvability. Learnable from examples. Evolvable. Parity functions. The core model: PAC . [Valiant . 84. ]. Learner observes random examples: . Randy . Goebel. Alberta Innovates Centre for Machine Learning. Department of Computing Science. University of Alberta. Edmonton, Alberta . Canada. rgoebel@ualberta.ca. Fuji-san. BIRS. Science or Engineering?. Asher Stern & . Ido. Dagan. ISCOL. June 2011, Israel. 1. Recognizing Textual Entailment (RTE). Given a text, . T. , and a hypothesis, . H. Does . T. entail . H. 2. T. : . An explosion caused by gas took place at a . (Excitement Project). Bernardo Magnini. (on behalf of the Excitement consortium). 1. STS workshop, NYC March 12-13 2012. Excitement Project. EXploring. Customer Interactions through Textual . EntailMENT. Peter Clark. Allen Institute for Artificial Intelligence (AI2). Mission: . achieve scientific breakthroughs by constructing AI systems with reasoning, learning, and reading capabilities. . 2. Overall Goals. Open-IE . meets . Knowledge . Representation. Ido Dagan. Bar-. Ilan. University, Israel. Knowledge Representation (KR). Two complementary frameworks:. Knowledge graphs. Formal pre-specified schema & predicates. July 7, 2017. Mary Mesnard, RHIA, CTR, Wilhelmina Ross, MPH, PA. Edits. Do you ever wonder where these edits originate?. Do you know how the State of Maryland creates edits metafiles?. Can you imagine the volume of edits that get released, changed, or deleted?. and . Distributional. Semantics. :. Between. . Syntactic. . Structures. and . Compositional. . Distributional. Semantics. Fabio Massimo . Zanzotto. ART Group. Dipartimento di Ingegneria dell’Impresa. Proofs. Asher Stern,. . Amnon. . Lotan. , . Shachar. . Mirkin. , . Eyal. . Shnarch. , . Lili. . Kotlerman. , Jonathan . Berant. . and . Ido. Dagan. TAC. November 2011, NIST, Gaithersburg, Maryland, USA. Omer Levy . Ido. Dagan Jacob Goldberger. Bar-. Ilan. University, . Israel. Open IE. Extracts propositions from text. “…which makes aspirin relieve headaches.”. No supervision. No pre-defined schema. Proofs. Asher Stern,. . Amnon. . Lotan. , . Shachar. . Mirkin. , . Eyal. . Shnarch. , . Lili. . Kotlerman. , Jonathan . Berant. . and . Ido. Dagan. TAC. November 2011, NIST, Gaithersburg, Maryland, USA. Asher Stern & . Ido. Dagan. ISCOL. June 2011, Israel. 1. Recognizing Textual Entailment (RTE). Given a text, . T. , and a hypothesis, . H. Does . T. entail . H. 2. T. : . An explosion caused by gas took place at a .

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