Recognizing Nested Named Entity in Biomedical Texts: A Neural Network Model with Multi-Task Learning

Many named entities usually contain nested entities in biomedical texts. Nested entities pose challenge to the task of named entity recognition. Traditional methods try to solve the problem as a graph-structure prediction problem. However, these methods fail to sufficiently capture the boundaries information between nested entities, which limits the performance of the task. In this paper, we take a different view by solving each unique entity type as a separate task, using multi-task learning with dispatched attention to facilitate information exchange between tasks. Results on GENIA corpus show that the proposed method is highly effective, obtaining the best results in the literature.