Topic-informed neural approach for biomedical event extraction.

Abstract:

:As a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods have been done on sentence-level event extraction, there are few works taking document context into account, losing potentially informative knowledge that is beneficial for trigger detection. In this paper, we propose a variational neural approach for biomedical event extraction, which can take advantage of latent topics underlying documents. By adopting a joint modeling manner of topics and events, our model is able to produce more meaningful and event-indicative words compare to prior topic models. In addition, we introduce a language model embeddings to capture context-dependent features. Experimental results show that our approach outperforms various baselines in a commonly used multi-level event extraction corpus.

journal_name

Artif Intell Med

authors

Zhang J,Liu M,Zhang Y

doi

10.1016/j.artmed.2019.101783

subject

Has Abstract

pub_date

2020-03-01 00:00:00

pages

101783

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(19)30923-6

journal_volume

103

pub_type

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