Learning to extract international relations from political context

Brendan O'Connor, Brandon Michael Stewart, Noah A. Smith

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

28 Scopus citations

Abstract

We describe a new probabilistic model for extracting events between major political actors from news corpora. Our unsupervised model brings together familiar components in natural language processing (like parsers and topic models) with contextual political information-temporal and dyad dependence- to infer latent event classes. We quantitatively evaluate the model's performance on political science benchmarks: recovering expert-assigned event class valences, and detecting real-world conflict. We also conduct a small case study based on our model's inferences.

Original languageEnglish (US)
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages1094-1104
Number of pages11
ISBN (Print)9781937284503
StatePublished - Jan 1 2013
Event51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
Duration: Aug 4 2013Aug 9 2013

Publication series

NameACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume1

Other

Other51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
CountryBulgaria
CitySofia
Period8/4/138/9/13

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language

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    O'Connor, B., Stewart, B. M., & Smith, N. A. (2013). Learning to extract international relations from political context. In Long Papers (pp. 1094-1104). (ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference; Vol. 1). Association for Computational Linguistics (ACL).