PPT-A Confidence Model for Syntactically-Motivated Entailment Proofs

Asher Stern amp 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 . 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. Proofs. Asher Stern,. . Amnon. . Lotan. , . Shachar. . Mirkin. , . Eyal. . Shnarch. , . Lili. . Kotlerman. , Jonathan . Berant. . and . Ido. Dagan. TAC. November 2011, NIST, Gaithersburg, Maryland, USA. 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. Educational . Data Mining. HUDK4050. Fall . 2015. Textbook/Readings. Detector Confidence. Any questions about detector confidence?. Detector Confidence. What is a detector confidence?. Detector Confidence. 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. Jeffrey W. Murray (jwmurray@vc. u.edu). . Thomas J. Nelson (tjnelson@vcu.edu). Virginia Commonwealth University . This practice session will discuss the “motivated sequence” as a highly effective and easily implementable pedagogical strategy that can increase student engagement and improve learning outcomes. Following illustrations of the motivational sequence as classroom practice and an open general discussion of the technique, participants will be invited to both share their own uses of the technique and also brainstorm potential applications of the “motivated sequence” to their own course assignments and classroom practices.. PROJECT MANAGER. PROS. Subject Matter Expert in one field. Task . driven . process. Ask a . lot. of questions. CONS. Subject Matter Expert in . only. one field. Not familiar with the . process. Ask . 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. The Model Checking Paradigm Ken McMillan Microsoft Research Overview The model checking paradigm Fundamental unresolved problems in the paradigm Strategies for resolving these problems What to look for in model checking talks Dan M. Kahan. Yale University. & 10^3s of others!. Fake News, Toxic. Meanings, and Identity-Protective Cognition. Fake news. . . . . . . Fake news. . . . . .. compliments of . . Macedonia. !. Are you looking to expand your real estate investment portfolio? Here are the best ways to find motivated seller leads in your area. At Motivated Leads, we help investors generate authentic motivated home seller leads using cost-effective digital marketing strategies, such as Google PPC and Facebook campaigns. Visit https://www.motivatedleads.com/ to learn more about our services.