Recognizing Nested Named Entity in Biomedical Texts: A Neural Network Model with MultiTask 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 graphstructure 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 multitask 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.

A neural networkbased joint learning approach for biomedical entity and relation extraction from biomedical literature Abstract   Recently joint modeling methods of entity and relation exhibit more promising results than traditional pipelined methods in general domain. However, they are inappropriate for the biomedical domain due to numerous overlapping relations in biomedical text. To alleviate the problem, we propose a neural networkbased joint learning approach for biomedical entity and relation extraction. In this approach, a novel tagging scheme that takes into account overlapping relations is proposed. Then the AttBiLSTMCRF model is built to jointly extract the entities and their relations with our extraction rules. Moreover, the contextualized ELMo representations pretrained on biomedical text are used to further improve the performance. Experimental results on biomedical corpora show that our method can significantly improve the performance of overlapping relation extraction and achieves the stateoftheart performance.