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    • AAAI 2016
      Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, and Stefano Ermon
      Many recent algorithms for approximate model counting are based on a reduction to combinatorial searches over random subsets of the space defined by parity or XOR constraints. Long parity constraints (involving many variables) provide strong theoretical guarantees but are computationally difficult…  (More)
    • AAAI 2016
      Shuo Yang, Tushar Khot, Kristian Kersting, and Sriraam Natarajan
      Many real world applications in medicine, biology, communication networks, web mining, and economics, among others, involve modeling and learning structured stochastic processes that evolve over continuous time. Existing approaches, however, have focused on propositional domains only. Without…  (More)
    • AAAI 2016
      Peter Clark, Oren Etzioni, Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, and Peter Turney
      What capabilities are required for an AI system to pass standard 4th Grade Science Tests? Previous work has examined the use of Markov Logic Networks (MLNs) to represent the requisite background knowledge and interpret test questions, but did not improve upon an information retrieval (IR) baseline…  (More)
    • WSDM 2016
      Bhavana Dalvi, Aditya Mishra, and William W. Cohen
      In an entity classification task, topic or concept hierarchies are often incomplete. Previous work by Dalvi et al. has shown that in non-hierarchical semi-supervised classification tasks, the presence of such unanticipated classes can cause semantic drift for seeded classes. The Exploratory…  (More)
    • Vanderbilt 2016
      Amitai Etzioni and Oren Etzioni
      AI programs make numerous decisions on their own, lack transparency, and may change frequently. Hence, the article shows, unassisted human agents — such as auditors, accountants, inspectors, and police — cannot ensure that AI guided instruments will abide by the law. Human agents need assistance of…  (More)
    • 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)