Viewing 18 papers from 2015
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    • ICCV 2015
      Hamid Izadinia, Fereshteh Sadeghi, Santosh Divvala, Hanna Hajishirzi, Yejin Choi, and Ali Farhadi
      We introduce Segment-Phrase Table (SPT), a large collection of bijective associations between textual phrases and their corresponding segmentations. Leveraging recent progress in object recognition and natural language semantics, we show how we can successfully build a highquality segment-phrase…  (More)
    • EMNLP 2015
      Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi, Oren Etzioni, and Clint Malcolm
      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 formal problem description likely to be…  (More)
    • AAAI • Workshop on Scholarly Big Data 2015
      Christopher Clark and Santosh Divvala
      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 articles. While many "off-the-shelf" tools…  (More)
    • Proceedings of IAAI 2015
      Peter Clark
      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 those answers. To reach this next…  (More)
    • AAAI • Workshop on Scholarly Big Data 2015
      Marco Valenzuela, Vu Ha, and Oren Etzioni
      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 research topics and in methods that…  (More)
    • NAACL 2015
      Rebecca Sharp, Peter Jansen, Mihai Surdeanu, and Peter Clark
      Monolingual alignment models have been shown to boost the performance of question answering systems by "bridging the lexical chasm" between questions and answers. The main limitation of these approaches is that they require semistructured training data in the form of question-answer pairs, which is…  (More)
    • NAACL 2015
      Ben Hixon, Peter Clark, and Hannaneh Hajishirzi
      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 uses the graph to solve questions. We are…  (More)
    • TACL 2015
      Daniel Fried, Peter Jansen, Gustave Hahn-Powell, Mihai Surdeanu, and Peter Clark
      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 such as question-answer pairs; neural…  (More)
    • CVPR 2015
      Fereshteh Sadeghi, Santosh Divvala, and Ali Farhadi
      How can we know whether a statement about our world is valid. For example, given a relationship between a pair of entities e.g., 'eat(horse, hay)', how can we know whether this relationship is true or false in general. Gathering such knowledge about entities and their relationships is one of the…  (More)
    • EMNLP 2015
      Tushar Khot, Niranjan Balasubramanian, Eric Gribkoff, Ashish Sabharwal, Peter Clark, and Oren Etzioni
      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 extraction, while scalable, often results…  (More)
    • EMNLP 2015
      Yang Li and Peter Clark
      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, and can be used to answer questions…  (More)
    • K-CAP • First International Workshop on Capturing Scientific Knowledge (SciKnow) 2015
      Samuel Louvan, Chetan Naik, Veronica Lynn, Ankit Arun, Niranjan Balasubramanian, and Peter Clark
      We consider a 4th grade level question answering task. We focus on a subset involving recognizing instances of physical, biological, and other natural processes. Many processes involve similar entities and are hard to distinguish using simple bag-of-words representations alone.
    • CPAIOR 2015
      Brian Kell, Ashish Sabharwal, and Willem-Jan van Hoeve
      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 decision diagrams (BDDs) and resolution in…  (More)
    • NIPS 2015
      Fereshteh Sadeghi, C. Lawrence Zitnick, and Ali Farhadi
      In this paper, we study the problem of answering visual analogy questions. These questions take the form of image A is to image B as image C is to what. Answering these questions entails discovering the mapping from image A to image B and then extending the mapping to image C and searching for the…  (More)
    • NIPS 2015
      Been Kim, Julie Shah, and Finale Doshi-Velez
      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 directly report a global set of…  (More)
    • TACL 2015
      Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang
      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 discriminative models. These models are trained…  (More)
    • CVPR 2015
      Mohammad Rastegari, Hannaneh Hajishirzi, and Ali Farhadi
      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 ranking such that positive (top rank…  (More)
    • ICCV 2015
      Bilge Soran, Ali Farhadi, and Linda Shapiro
      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 issuing notifications on actions that may…  (More)