Joint inference for cross-document information extraction

Qi Li, Sam Anzaroot, Wen Pin Lin, Xiang Li, Heng Ji

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

Abstract

Previous information extraction (IE) systems are typically organized as a pipeline architecture of separated stages which make independent local decisions. When the data grows beyond some certain size, the extracted facts become inter-dependent and thus we can take advantage of information redundancy to conduct reasoning across documents and improve the performance of IE. We describe a joint inference approach based on information network structure to conduct cross-fact reasoning with an integer linear programming framework. Without using any additional labeled data this new method obtained 13.7%-24.4% user browsing cost reduction over a state-of-the-art IE system which extracts various types of facts independently.

Original languageEnglish (US)
Title of host publicationCIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
Pages2225-2228
Number of pages4
DOIs
StatePublished - Dec 13 2011
Externally publishedYes
Event20th ACM Conference on Information and Knowledge Management, CIKM'11 - Glasgow, United Kingdom
Duration: Oct 24 2011Oct 28 2011

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other20th ACM Conference on Information and Knowledge Management, CIKM'11
CountryUnited Kingdom
CityGlasgow
Period10/24/1110/28/11

Keywords

  • global reasoning
  • information extraction
  • integer linear programming

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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  • Cite this

    Li, Q., Anzaroot, S., Lin, W. P., Li, X., & Ji, H. (2011). Joint inference for cross-document information extraction. In CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management (pp. 2225-2228). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/2063576.2063932