Biomedical Event Extraction based on Knowledge-driven Tree-LSTM

Diya Li, Lifu Huang, Heng Ji, Jiawei Han

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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.
Anthology ID:
N19-1145
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1421–1430
Language:
URL:
https://aclanthology.org/N19-1145
DOI:
10.18653/v1/N19-1145
Bibkey:
Cite (ACL):
Diya Li, Lifu Huang, Heng Ji, and Jiawei Han. 2019. Biomedical Event Extraction based on Knowledge-driven Tree-LSTM. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1421–1430, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Biomedical Event Extraction based on Knowledge-driven Tree-LSTM (Li et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/N19-1145.pdf
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