DAAN: A Dictionary-Based Adaptive Attention Network for Biomedical Named Entity Recognition with Chinese Electronic Medical Records

Biomedical named entity recognition (BNER) is a basic task of the extraction of medical information. The existing deep learning-based approaches usually represent the medical text by using words or characters. However, most of biomedical terms consist of many words (characters). Splitting them into many fragments (words or characters) while leveraging the attention mechanism to assign attention scores for each fragment maybe disperse the importance weight and cause a lower attention score for the biomedical terms. Therefore, this paper presents a dictionary-based adaptive attention network for BNER. Specifically, a biomedical dictionary is firstly constructed by integrating multiple existing medical resources. Secondly, building the guidance vectors by matching the electronic medical record (EMR) text to the constructed dictionary. Then, an adaptive attention strategy is presented to guide the attention mechanism to assign higher attention to the overall medical term by using the guidance vectors. We conduct extensive experiments on a real-world dataset, the results illuminate that our presented method outperforms all baselines.