Papers

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Viewing 721-730 of 758 papers
  • Solving Geometry Problems: Combining Text and Diagram Interpretation

    Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi, Oren Etzioni, and Clint MalcolmEMNLP2015 This paper introduces GeoS, the first automated system to solve unaltered SAT geometry questions by combining text understanding and diagram interpretation. We model the problem of understanding geometry questions as submodular optimization, and identify a…
  • Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge

    Yang Li and Peter ClarkEMNLP2015 Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting what text appears to convey. The scene constitutes an understanding of the text…
  • BDD-Guided Clause Generation

    Brian Kell, Ashish Sabharwal, and Willem-Jan van HoeveCPAIOR2015 Nogood learning is a critical component of Boolean satisfiability (SAT) solvers, and increasingly popular in the context of integer programming and constraint programming. We present a generic method to learn valid clauses from exact or approximate binary…
  • Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning

    Mohammad Rastegari, Hannaneh Hajishirzi, and Ali FarhadiCVPR2015 In this paper we present a bottom-up method to instance level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities. We discover positive instances by optimizing for a…
  • Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge!

    Peter ClarkProceedings of IAAI2015 While there has been an explosion of impressive, datadriven AI applications in recent years, machines still largely lack a deeper understanding of the world to answer questions that go beyond information explicitly stated in text, and to explain and discuss…
  • Exploring Markov Logic Networks for Question Answering

    Tushar Khot, Niranjan Balasubramanian, Eric Gribkoff, Ashish Sabharwal, Peter Clark, and Oren EtzioniEMNLP2015 Elementary-level science exams pose significant knowledge acquisition and reasoning challenges for automatic question answering. We develop a system that reasons with knowledge derived from textbooks, represented in a subset of first-order logic. Automatic…
  • Generating Notifications for Missing Actions: Don’t forget to turn the lights off!

    Bilge Soran, Ali Farhadi, and Linda ShapiroICCV2015 We all have experienced forgetting habitual actions among our daily activities. For example, we probably have forgotten to turn the lights off before leaving a room or turn the stove off after cooking. In this paper, we propose a solution to the problem of…
  • Higher-order Lexical Semantic Models for Non-factoid Answer Reranking

    Daniel Fried, Peter Jansen, Gustave Hahn-Powell, Mihai Surdeanu, and Peter ClarkTACL2015 Lexical semantic models provide robust performance for question answering, but, in general, can only capitalize on direct evidence seen during training. For example, monolingual alignment models acquire term alignment probabilities from semistructured data…
  • Identifying Meaningful Citations

    Marco Valenzuela, Vu Ha, and Oren EtzioniAAAI • Workshop on Scholarly Big Data2015 We introduce the novel task of identifying important citations in scholarly literature, i.e., citations that indicate that the cited work is used or extended in the new effort. We believe this task is a crucial component in algorithms that detect and follow…
  • Learning Knowledge Graphs for Question Answering through Conversational Dialog

    Ben Hixon, Peter Clark, and Hannaneh HajishirziNAACL2015 We describe how a question-answering system can learn about its domain from conversational dialogs. Our system learns to relate concepts in science questions to propositions in a fact corpus, stores new concepts and relations in a knowledge graph (KG), and…