Pocket knowledge base population

Travis Wolfe, Mark Dredze, Benjamin Van Durme

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage, leading to sparse KBs. We propose Pocket Knowledge Base Population (PKBP), the task of dynamically constructing a KB of entities related to a query and finding the best characterization of relationships between entities. We describe novel Open Information Extraction methods which leverage the PKB to find informative trigger words. We evaluate using existing KBP shared-task data as well as new annotations collected for this work. Our methods produce high quality KBs from just text with many more entities and relationships than existing KBP systems.

Original languageEnglish (US)
Title of host publicationACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers)
PublisherAssociation for Computational Linguistics (ACL)
Pages305-310
Number of pages6
ISBN (Electronic)9781945626760
DOIs
StatePublished - 2017
Event55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada
Duration: Jul 30 2017Aug 4 2017

Publication series

NameACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Volume2

Conference

Conference55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
Country/TerritoryCanada
CityVancouver
Period7/30/178/4/17

ASJC Scopus subject areas

  • Language and Linguistics
  • Artificial Intelligence
  • Software
  • Linguistics and Language

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