This before that: Causal precedence in the biomedical domain

Gus Hahn-Powell, Dane Bell, Marco A. Valenzuela-Escárcega, Mihai Surdeanu

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

3 Scopus citations

Abstract

Causal precedence between biochemical interactions is crucial in the biomedical domain, because it transforms collections of individual interactions, e.g., bindings and phosphorylations, into the causal mechanisms needed to inform meaningful search and inference. Here, we analyze causal precedence in the biomedical domain as distinct from open-domain, temporal precedence. First, we describe a novel, hand-annotated text corpus of causal precedence in the biomedical domain. Second, we use this corpus to investigate a battery of models of precedence, covering rule-based, feature-based, and latent representation models. The highestperforming individual model achieved a micro F1 of 43 points, approaching the best performers on the simpler temporalonly precedence tasks. Feature-based and latent representation models each outperform the rule-based models, but their performance is complementary to one another. We apply a sieve-based architecture to capitalize on this lack of overlap, achieving a micro F1 score of 46 points.

Original languageEnglish (US)
Title of host publicationBioNLP 2016 - Proceedings of the 15th Workshop on Biomedical Natural Language Processing
EditorsKevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Jun-ichi Tsujii
PublisherAssociation for Computational Linguistics (ACL)
Pages146-155
Number of pages10
ISBN (Electronic)9781945626128
StatePublished - 2016
Event15th Workshop on Biomedical Natural Language Processing, BioNLP 2016 - Berlin, Germany
Duration: Aug 12 2016 → …

Publication series

NameBioNLP 2016 - Proceedings of the 15th Workshop on Biomedical Natural Language Processing

Conference

Conference15th Workshop on Biomedical Natural Language Processing, BioNLP 2016
Country/TerritoryGermany
CityBerlin
Period8/12/16 → …

ASJC Scopus subject areas

  • Biomedical Engineering
  • Language and Linguistics
  • Information Systems
  • Software
  • Health Informatics
  • Computer Science Applications

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