Biomedical event trigger extraction based on multi-layer residual BiLSTM and contextualized word representations

Abstract

Biomedical event extraction is an important branch of biomedical information extraction. Trigger extraction is the most essential sub-task in event extraction, which has been widely concerned. Existing trigger extraction studies are mostly based on conventional machine learning or neural networks. But they neglect the ambiguity of word representations and the insufficient feature extraction by shallow hidden layers. In this paper, trigger extraction is treated as a sequence labeling problem. We introduce the language model to dynamically compute contextualized word representations and propose a multi-layer residual bidirectional long short-term memory (BiLSTM) architecture. First, we concatenate contextualized word embedding, pretrained word embedding and character-level embedding as the feature representations, which effectively solves the tokens’ ambiguity in biomedical corpora. Then, the designed BiLSTM block with residual connection and gated multi-layer perceptron is adopted to extract features iteratively. This architecture improves the ability of our model to capture information and avoids gradient exploding or vanishing. Finally, we combine the multi-layer residual BiLSTM with CRF layer to obtain more reasonable label sequences. Comparing with other state-of-the-art methods, the proposed model achieves the competitive performance (F1-score: 80.74%) on the biomedical multi-level event extraction (MLEE) corpus without any manual participation and feature engineering.