Biomedical Event Trigger Detection Based on BiLSTM Integrating Attention Mechanism and Sentence Vector | IEEE Conference Publication | IEEE Xplore

Biomedical Event Trigger Detection Based on BiLSTM Integrating Attention Mechanism and Sentence Vector


Abstract:

As the crucial and prerequisite step in biomedical event extraction, trigger detection has attracted much attention. Most of the existing trigger detection methods either...Show More

Abstract:

As the crucial and prerequisite step in biomedical event extraction, trigger detection has attracted much attention. Most of the existing trigger detection methods either rely on elaborately designed features or consider features only within a window. Another challenge is that the existing methods treat each word in sentence equally. Also, most methods ignore the sentence-level semantic information. Therefore, we propose a trigger detection method based on Bidirectional Long Short Term Memory (BiLSTM) neural network, which can skip manual complex feature extraction. Furthermore, to obtain more semantic and syntactic information, we train dependency-based word embeddings to represent words, and add sentence vector to enrich sentence-level features. Finally, we integrate attention mechanism to capture the most important semantic information in a sentence. The experimental results on the multi-level event extraction (MLEE) corpus show that the proposed method outperforms the state-of-the-art systems.
Date of Conference: 03-06 December 2018
Date Added to IEEE Xplore: 24 January 2019
ISBN Information:
Conference Location: Madrid, Spain

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