Low Resource Causal Event Detection from Biomedical Literature

Zhengzhong Liang, Enrique Noriega-Atala, Clayton Morrison, Mihai Surdeanu

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

7 Scopus citations

Abstract

Recognizing causal precedence relations among the chemical interactions in biomedical literature is crucial to understanding the underlying biological mechanisms. However, detecting such causal relation can be hard because: (1) many times, such causal relations among events are not explicitly expressed by certain phrases but implicitly implied by very diverse expressions in the text, and (2) annotating such causal relation detection datasets requires considerable expert knowledge and effort. In this paper, we propose a strategy to address both challenges by training neural models with in-domain pre-training and knowledge distillation. We show that, by using very limited amount of labeled data, and sufficient amount of unlabeled data, the neural models outperform previous baselines on the causal precedence detection task, and are ten times faster at inference compared to the BERT base model.

Original languageEnglish (US)
Title of host publicationBioNLP 2022 @ ACL 2022 - Proceedings of the 21st Workshop on Biomedical Language Processing
PublisherAssociation for Computational Linguistics (ACL)
Pages252-263
Number of pages12
ISBN (Electronic)9781955917278
StatePublished - 2022
Event21st Workshop on Biomedical Language Processing, BioNLP 2022 at the Association for Computational Linguistics Conference, ACL 2022 - Dublin, Ireland
Duration: May 26 2022 → …

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference21st Workshop on Biomedical Language Processing, BioNLP 2022 at the Association for Computational Linguistics Conference, ACL 2022
Country/TerritoryIreland
CityDublin
Period5/26/22 → …

ASJC Scopus subject areas

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

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