Multi-Level Attention Based BLSTM Neural Network for Biomedical Event Extraction

Xinyu HE  Lishuang LI  Xingchen SONG  Degen HUANG  Fuji REN  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.9   pp.1842-1850
Publication Date: 2019/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7268
Type of Manuscript: PAPER
Category: Natural Language Processing
event extraction,  trigger detection,  argument detection,  BLSTM neural network,  multi-level attention,  

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Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.