Biomedical event extraction based on knowledge-driven tree-LSTM

Diya Li, Lifu Huang, Heng Ji, Jiawei Han

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

Abstract

Event extraction for the biomedical domain is more challenging than that in the general news domain since it requires broader acquisition of domain-specific knowledge and deeper understanding of complex contexts. To better encode contextual information and external background knowledge, we propose a novel knowledge base (KB)-driven tree-structured long short-term memory networks (Tree-LSTM) framework, incorporating two new types of features: (1) dependency structures to capture wide contexts; (2) entity properties (types and category descriptions) from external ontologies via entity linking. We evaluate our approach on the BioNLP shared task with Genia dataset and achieve a new state-of-the-art result. In addition, both quantitative and qualitative studies demonstrate the advancement of the Tree-LSTM and the external knowledge representation for biomedical event extraction.

Original languageEnglish (US)
Title of host publicationLong and Short Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages1421-1430
Number of pages10
ISBN (Electronic)9781950737130
StatePublished - Jan 1 2019
Event2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 7 2019

Publication series

NameNAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume1

Conference

Conference2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
CountryUnited States
CityMinneapolis
Period6/2/196/7/19

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science Applications
  • Linguistics and Language

Fingerprint Dive into the research topics of 'Biomedical event extraction based on knowledge-driven tree-LSTM'. Together they form a unique fingerprint.

  • Cite this

    Li, D., Huang, L., Ji, H., & Han, J. (2019). Biomedical event extraction based on knowledge-driven tree-LSTM. In Long and Short Papers (pp. 1421-1430). (NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference; Vol. 1). Association for Computational Linguistics (ACL).