Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

Background: Bio-entity Coreference resolution is an important task to gain a complete understanding of biomedical texts automatically. Previous neural network-based studies on this topic are domain system based methods which rely on some domain-specific information integration. However, for the identical mentions, this may lead to misleading information, as the model tends to get similar or even identical representations, which further leads to wrongful predictions. Results: we propose a new context-aware Feature Attention model to distinguish identical mentions effectively to better resolve coreference. The new model can represent identical mentions based on different contexts by adaptively exploiting features effectively. The proposed model substantially outperforms the state-of-the-art baselines on the BioNLP dataset with a 64.0% F1 score and further demonstrates superior performance on the differential representation and coreferential link of identical mentions. Conclusion: The context-aware Feature Attention model adaptively exploit features and represent identical mentions according to different contexts, which significantly makes the system obtain semantic information effectively and make more accurate predictions. Considering that this approach is still limited when context information is insufficient, we expect to utilize such information more fine-grained with the help of the external knowledge base in coreference resolution.