AERNs: AttentionBased Entity Region Networks for MultiGrained Named Entity Recognition Sequential labelingbased named entity recognition approaches restrict each word belonging to at most one entity region, which will have a problem when recognizing the nested named entities. Various models for nested named entity recognition are designed explicitly and usually do not perform well on nonoverlapping named entity recognition compared to sequence labeling models. To tackle the aforementioned problem, we propose a novel model named AttentionBased Entity Region Networks (AERNs) for multigrained entity recognition where multiple entities in a sentence could be nonoverlapping or nested. AERNs consist of an entity region recognizer that examines all possible entity regions and an entity region classifier for classifying the regions. Context information is incorporated to improve the performance of named entity recognition by using an attention mechanism that helps the model focus on entityrelated context information. Experiments show that AERNs can effectively recognize both nested and nonoverlapping entities, and improves the stateoftheart result by around 3% on several benchmarks datasets.

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.