Attention guided capsule networks for chemical-protein interaction extraction.

The biomedical literature contains a sufficient number of chemical-protein interactions (CPIs). Automatic extraction of CPI is a crucial task in the biomedical domain, which has excellent benefits for precision medicine, drug discovery and basic biomedical research. In this study, we propose a novel model, BERT-based attention-guided capsule networks (BERT-Att-Capsule), for CPI extraction. Specifically, the approach first employs BERT (Bidirectional Encoder Representations from Transformers) to capture the long-range dependencies and bidirectional contextual information of input tokens. Then, the aggregation is regarded as a routing problem for how to pass messages from source capsule nodes to target capsule nodes. This process enables capsule networks to determine what and how much information need to be transferred, as well as to identify sophisticated and interleaved features. Afterwards, the multi-head attention is applied to guide the model to learn different contribution weights of capsule networks obtained by the dynamic routing. We evaluate our model on the CHEMPROT corpus. Our approach is superior in performance as compared with other state-of-the-art methods. Experimental results show that our approach can adequately capture the long-range dependencies and bidirectional contextual information of input tokens, obtain more fine-grained aggregation information through attention-guided capsule networks, and therefore improve the performance.