Papers

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Viewing 771-780 of 805 papers
  • 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…
  • Looking Beyond Text: Extracting Figures, Tables and Captions from Computer Science Papers

    Christopher Clark and Santosh DivvalaAAAI • Workshop on Scholarly Big Data2015 Identifying and extracting figures and tables along with their captions from scholarly articles is important both as a way of providing tools for article summarization, and as part of larger systems that seek to gain deeper, semantic understanding of these…
  • Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction

    Been Kim, Julie Shah, and Finale Doshi-VelezNIPS2015 We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretability and to…
  • Parsing Algebraic Word Problems into Equations

    Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas AngTACL2015 This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global…