PPT-From Textual Entailment to Knowledgeable Machines

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. 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. University . of. . Rome. “Tor Vergata”. Roma, Italy. Textual Entailment Recognition for Web Based Question-Answering. Operational. . Scenarios. What’s. the . weather. in Macao?. When. . is. Resources: . A Survey of Paraphrasing and Textual Entailment . Methods. . . . Androutsopoulos. and . Malakasiotis. Natural Language and Meaning. Meaning. Language. Ambiguity. 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. By:Loay. . A.Hammad. I am knowledgeable because I know a lot.. When people wants help from me I help them by. advising . them and advisement needs. knowledge. . . I am knowledgeable . If you want to be knowledgeable you should read a lot of books . 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?. (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. an Existing Graphical . Modeling . Language. : . Experience Report . with GRL. . Vahdat Abdelzad, . Daniel Amyot. , . Timothy . Lethbridge. University of Ottawa, . Canada. damyot@uottawa.ca. SDL 2015, Berlin, October 13. Recognizing. . Textual. . Entailment. (RTE) . Italian. . Chapter. Johan Bos. 1. , Fabio Massimo Zanzotto. 2. , Marco Pennacchiotti. 3. 1. University . of. . Rome. “La Sapienza”, Italy. 2. University of Rome “Tor Vergata”, Italy. and . Distributional. Semantics. :. Between. . Syntactic. . Structures. and . Compositional. . Distributional. Semantics. Fabio Massimo . Zanzotto. ART Group. Dipartimento di Ingegneria dell’Impresa. 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. algorithms in. Question Answering. Alexander . Solovyev. Bauman Moscow Sate Technical University. a-soloviev@mail.ru. 20.10.2011. 1. RCDL. Voronezh.. Agenda. Question Answering and Answer Validation task. algorithms in. Question Answering. Alexander . Solovyev. Bauman Moscow Sate Technical University. a-soloviev@mail.ru. 20.10.2011. 1. RCDL. Voronezh.. Agenda. Question Answering and Answer Validation task. When we read, we are often asked to answer questions or express our ideas about the text.. Why use Explicit Textual Evidence. In order to let people know that we aren’t just making stuff up, we should always use Explicit Textual Evidence to support our answers, ideas, or opinions about texts we read..