Biomedical Event Extraction as Sequence Labeling

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

Biomedical Event Extraction as Sequence Labeling

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

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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.

Originalsprog	Engelsk
Titel	Conference on Empirical Methods in Natural Language Processing
Forlag	Association for Computational Linguistics
Publikationsdato	nov. 2020
Status	Udgivet - nov. 2020

Emneord

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

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EMNLP2020__BEESLAccepteret manuskript, 457 KB

https://www.aclweb.org/anthology/2020.emnlp-main.431/

Multi-Task Sequence Labeling Under Adverse Conditions
Plank, B. (PI) & van der Goot, R. (CoI)
Amazon.com, Inc.
01/04/2019 → 31/08/2020
Projekter: Projekt › Andet

Citationsformater

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title = "Biomedical Event Extraction as Sequence Labeling",

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.",

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year = "2020",

month = nov,

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booktitle = "Conference on Empirical Methods in Natural Language Processing",

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T1 - Biomedical Event Extraction as Sequence Labeling

AU - Ramponi, Alan

AU - van der Goot, Rob

AU - Lombardo, Rosario

AU - Plank, Barbara

PY - 2020/11

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N2 - 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.

AB - 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.

KW - Biomedical Event Extraction

KW - Sequence Labeling

KW - Multi-task Learning

KW - End-to-End Neural Model

KW - Genia 2011 Benchmark

KW - Biomedical Event Extraction

KW - Sequence Labeling

KW - Multi-task Learning

KW - End-to-End Neural Model

KW - Genia 2011 Benchmark

M3 - Article in proceedings

BT - Conference on Empirical Methods in Natural Language Processing

PB - Association for Computational Linguistics

ER -

Biomedical Event Extraction as Sequence Labeling

Abstract

Emneord

Adgang til dokumentet

Fingeraftryk

Projekter

Multi-Task Sequence Labeling Under Adverse Conditions

Citationsformater