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Open Question Answering Over Curated and Extracted Knowledge Bases
Anthony Fader, Luke Zettlemoyer, and Oren EtzioniKDD • 2014 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…Discourse Complements Lexical Semantics for Non-factoid Answer Reranking
Peter Jansen, Mihai Surdeanu, and Peter ClarkACL • 2014 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…A Lightweight and High Performance Monolingual Word Aligner
Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkACL • 2013 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…Automatic Coupling of Answer Extraction and Information Retrieval
Xuchen Yao, Benjamin Van Durme, and Peter ClarkACL • 2013 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…Answer Extraction as Sequence Tagging with Tree Edit Distance
Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkNAACL • 2013 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…A Study of the Knowledge Base Requirements for Passing an Elementary Science Test
Peter Clark, Phil Harrison, and Niranjan BalasubramanianCIKM • AKBC • 2013 Our 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…Extracting Meronyms for a Biology Knowledge Base Using Distant Supervision
Xiao Ling, Dan Weld, and Peter ClarkAKBC • 2013 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…Learning Biological Processes with Global Constraints
Aju Thalappillil Scaria, Jonathan Berant, Mengqiu Wang, Christopher D. Manning, Justin Lewis, Brittany Harding, and Peter ClarkEMNLP • 2013 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…Semi-Markov Phrase-based Monolingual Alignment
Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkEMNLP • 2013 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…Constructing a Textual KB from a Biology TextBook
Peter Clark, Phil Harrison, Niranjan Balasubramanian, and Oren EtzioniNAACL-HLT • AKBC Workshop • 2012 As 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…