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    • 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)
    • ACL 2014
      Peter Jansen, Mihai Surdeanu, and Peter Clark
      We propose a robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers, and a deep one based on Rhetorical Structure Theory. We evaluate…  (More)
    • ACL 2013
      Xuchen Yao, Benjamin Van Durme, and Peter Clark
      Information Retrieval (IR) and Answer Extraction are often designed as isolated or loosely connected components in Question Answering (QA), with repeated overengineering on IR, and not necessarily performance gain for QA. We propose to tightly integrate them by coupling automatically learned…  (More)
    • ACL 2013
      Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter Clark
      Fast alignment is essential for many natural language tasks. But in the setting of monolingual alignment, previous work has not been able to align more than one sentence pair per second. We describe a discriminatively trained monolingual word aligner that uses a Conditional Random Field to globally…  (More)
    • NAACL 2013
      Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter Clark
      Our goal is to extract answers from preretrieved sentences for Question Answering (QA). We construct a linear-chain Conditional Random Field based on pairs of questions and their possible answer sentences, learning the association between questions and answer types. This casts answer extraction as…  (More)
    • EMNLP 2013
      Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter Clark
      We introduce a novel discriminative model for phrase-based monolingual alignment using a semi-Markov CRF. Our model achieves stateof-the-art alignment accuracy on two phrasebased alignment datasets (RTE and paraphrase), while doing significantly better than other strong baselines in both non…  (More)
    • AKBC 2013
      Xiao Ling, Dan Weld, and Peter Clark
      Knowledge of objects and their parts, meronym relations, are at the heart of many question-answering systems, but manually encoding these facts is impractical. Past researchers have tried hand-written patterns, supervised learning, and bootstrapped methods, but achieving both high precision and…  (More)
    • EMNLP 2013
      Aju Thalappillil Scaria, Jonathan Berant, Mengqiu Wang, Christopher D. Manning, Justin Lewis, Brittany Harding, and Peter Clark
      Biological processes are complex phenomena involving a series of events that are related to one another through various relationships. Systems that can understand and reason over biological processes would dramatically improve the performance of semantic applications involving inference such as…  (More)