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    • 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)
    • EACL 2014
      Yuen-Hsien Tseng, Lung-Hao Lee, Shu-Yen Lin, Bo-Shun Liao, Mei-Jun Liu, Hsin-Hsi Chen, Oren Etzioni, and Anthony Fader
      This study presents the Chinese Open Relation Extraction (CORE) system that is able to extract entity-relation triples from Chinese free texts based on a series of NLP techniques, i.e., word segmentation, POS tagging, syntactic parsing, and extraction rules. We employ the proposed CORE techniques…  (More)
    • CVPR 2014
      Santosh K. Divvala, Ali Farhadi, and Carlos Guestrin
      Recognition is graduating from labs to real-world applications. While it is encouraging to see its potential being tapped, it brings forth a fundamental challenge to the vision researcher: scalability. How can we learn a model for any concept that exhaustively covers all its appearance variations…  (More)
    • ACL • Workshop on Semantic Parsing 2014
      Xuchen Yao, Jonathan Berant, and Benjamin Van Durme
      We contrast two seemingly distinct approaches to the task of question answering (QA) using Freebase: one based on information extraction techniques, the other on semantic parsing. Results over the same test-set were collected from two state-ofthe-art, open-source systems, then analyzed in…  (More)
    • KDD 2014
      Anthony Fader, Luke Zettlemoyer, and Oren Etzioni
      We consider the problem of open-domain question answering (Open QA) over massive knowledge bases (KBs). Existing approaches use either manually curated KBs like Freebase or KBs automatically extracted from unstructured text. In this paper, we present oqa, the first approach to leverage both curated…  (More)
    • Big Data 2014
      Foster Provost, Geoffrey I. Webb, Ron Bekkerman, Oren Etzioni, Usama Fayyad, and Claudia Perlich
      In August 2013, we held a panel discussion at the KDD 2013 conference in Chicago on the subject of data science, data scientists, and start-ups. KDD is the premier conference on data science research and practice. The panel discussed the pros and cons for top-notch data scientists of the hot data…  (More)
    • EMNLP 2014
      Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate Kushman
      This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant variables and their values. ARIS then maps this information into an equation that represents the problem, and…  (More)
    • Award Best Paper Award
      EMNLP 2014
      Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Brad Huang, Christopher D. Manning, Abby Vander Linden, Brittany Harding, and Peter Clark
      Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new reading comprehension task that requires complex reasoning over a single document. The input is a paragraph…  (More)
    • Award Best Paper Award
      AKBC 2014
      Peter Clark, Niranjan Balasubramanian, Sumithra Bhakthavatsalam, Kevin Humphreys, Jesse Kinkead, Ashish Sabharwal, and Oyvind Tafjord
      While there has been tremendous progress in automatic database population in recent years, most of human knowledge does not naturally fit into a database form. For example, knowledge that "metal objects can conduct electricity" or "animals grow fur to help them stay warm" requires a substantially…  (More)
    • International Conference on Principles and Practice of Constraint Programming 2014
      Ashish Sabharwal and Horst Samulowitz
      Novel search space splitting techniques have recently been successfully exploited to paralleliz Constraint Programming and Mixed Integer Programming solvers. We first show how universal hashing can be used to extend one such interesting approach to a generalized setting that goes beyond discrepancy…  (More)
    • AAAI 2014
      Min Joon Seo, Hannaneh Hajishirzi, Ali Farhadi, and Oren Etzioni
      Automatically solving geometry questions is a longstanding AI problem. A geometry question typically includes a textual description accompanied by a diagram. The first step in solving geometry questions is diagram understanding, which consists of identifying visual elements in the diagram, their…  (More)