Allen Institute for AI


Viewing 371-380 of 380 papers
  • Diagram Understanding in Geometry Questions

    Min Joon Seo, Hannaneh Hajishirzi, Ali Farhadi, and Oren EtzioniAAAI2014Automatically 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
  • Discourse Complements Lexical Semantics for Non-factoid Answer Reranking

    Peter Jansen, Mihai Surdeanu, and Peter ClarkACL2014We 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
  • A Lightweight and High Performance Monolingual Word Aligner

    Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkACL2013Fast 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
  • Automatic Coupling of Answer Extraction and Information Retrieval

    Xuchen Yao, Benjamin Van Durme, and Peter ClarkACL2013Information 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
  • Answer Extraction as Sequence Tagging with Tree Edit Distance

    Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkNAACL2013Our 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
  • Semi-Markov Phrase-based Monolingual Alignment

    Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkEMNLP2013We 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
  • Extracting Meronyms for a Biology Knowledge Base Using Distant Supervision

    Xiao Ling, Dan Weld, and Peter ClarkAKBC2013Knowledge 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
  • Learning Biological Processes with Global Constraints

    Aju Thalappillil Scaria, Jonathan Berant, Mengqiu Wang, Christopher D. Manning, Justin Lewis, Brittany Harding, and Peter ClarkEMNLP2013Biological 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
  • A Study of the Knowledge Base Requirements for Passing an Elementary Science Test

    Peter Clark, Phil Harrison, and Niranjan BalasubramanianCIKM • AKBC2013Our long-term interest is in machines that contain large amounts of general and scientific knowledge, stored in a "computable" form that supports reasoning and explanation. As a medium-term focus for this, our goal is to have the computer pass a fourth-grade science test, anticipating that much of… more
  • Constructing a Textual KB from a Biology TextBook

    Peter Clark, Phil Harrison, Niranjan Balasubramanian, and Oren EtzioniNAACL-HLT • AKBC Workshop2012As part of our work on building a "knowledgeable textbook" about biology, we are developing a textual question-answering (QA) system that can answer certain classes of biology questions posed by users. In support of that, we are building a "textual KB" - an assembled set of semi-structured… more