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Abstract
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.
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 language | English |
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Title of host publication | Conference on Empirical Methods in Natural Language Processing |
Publisher | Association for Computational Linguistics |
Publication date | Nov 2020 |
Publication status | Published - Nov 2020 |
Keywords
- Biomedical Event Extraction
- Sequence Labeling
- Multi-task Learning
- End-to-End Neural Model
- Genia 2011 Benchmark
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Dive into the research topics of 'Biomedical Event Extraction as Sequence Labeling'. Together they form a unique fingerprint.Projects
- 1 Finished
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Multi-Task Sequence Labeling Under Adverse Conditions
Plank, B. (PI) & van der Goot, R. (CoI)
01/04/2019 → 31/08/2020
Project: Other