Biomedical Event Extraction as Sequence Labeling

Alan Ramponi, Rob van der Goot, Rosario Lombardo, Barbara Plank

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


We introduce Biomedical Event Extraction as Sequence Labeling (BeeSL), a joint end-to-end neural information extraction model. BeeSL recasts the task as sequence labeling, taking advantage of a multi-label aware encoding strategy and jointly modeling the intermediate tasks via multi-task learning. BeeSL is fast, accurate, end-to-end, and unlike current methods does not require any external knowledge base or preprocessing tools. BeeSL outperforms the current best system on the Genia 2011 benchmark by 1.57 % absolute F1 score reaching 60.22 % F1, establishing a new state of the art for the task.
Importantly, we also provide first results on biomedical event extraction without gold entity information. Empirical results show that BeeSL's speed and accuracy makes it a viable approach for large-scale real-world scenarios.
Original languageEnglish
Title of host publicationConference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics
Publication dateNov 2020
Publication statusPublished - Nov 2020


  • Biomedical Event Extraction
  • Sequence Labeling
  • Multi-task Learning
  • End-to-End Neural Model
  • Genia 2011 Benchmark


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