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Open information extraction using Wikipedia

Authors:

Daniel S. WeldAuthors Info & Claims

ACL '10: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

Pages 118 - 127

Published: 11 July 2010 Publication History

Abstract

Information-extraction (IE) systems seek to distill semantic relations from natural-language text, but most systems use supervised learning of relation-specific examples and are thus limited by the availability of training data. Open IE systems such as TextRunner, on the other hand, aim to handle the unbounded number of relations found on the Web. But how well can these open systems perform?

This paper presents WOE, an open IE system which improves dramatically on TextRunner's precision and recall. The key to WOE's performance is a novel form of self-supervised learning for open extractors -- using heuristic matches between Wikipedia infobox attribute values and corresponding sentences to construct training data. Like TextRunner, WOE's extractor eschews lexicalized features and handles an unbounded set of semantic relations. WOE can operate in two modes: when restricted to POS tag features, it runs as quickly as TextRunner, but when set to use dependency-parse features its precision and recall rise even higher.

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Open information extraction using Wikipedia

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ACL '10: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

July 2010

1618 pages

Program Chair:
Jan Hajič
Charles University in Prague, Czech Republic

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 11 July 2010

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Cited By

Cao JFang JMeng ZLiang S(2024)Knowledge Graph Embedding: A Survey from the Perspective of Representation SpacesACM Computing Surveys10.1145/364380656:6(1-42)Online publication date: 2-Feb-2024
https://dl.acm.org/doi/10.1145/3643806
Zhang ZWinn RZhao YYu THalfond WJust RFraser G(2023)Automatically Reproducing Android Bug Reports using Natural Language Processing and Reinforcement LearningProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598066(411-422)Online publication date: 12-Jul-2023
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https://dl.acm.org/doi/10.1145/3451167
Qi SZheng LShang F(2021)Dependency Parsing-based Entity Relation Extraction over Chinese Complex TextACM Transactions on Asian and Low-Resource Language Information Processing10.1145/345027320:4(1-34)Online publication date: 9-Jun-2021
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Wang HTao QDu SLuo X(2020)An Extensible Framework of Leveraging Syntactic Skeleton for Semantic Relation ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/340288519:6(1-21)Online publication date: 27-Sep-2020
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Paşca MZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Approximate Definitional Constructs as Lightweight Evidence for Detecting Classes Among Wikipedia ArticlesProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3358167(2373-2376)Online publication date: 3-Nov-2019
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Pasca MWolfe TCulpepper JMoffat ABennett PLerman K(2019)Lightweight Lexical and Semantic Evidence for Detecting Classes Among Wikipedia ArticlesProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291020(78-86)Online publication date: 30-Jan-2019
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