Combined Selfattention Mechanism For Biomedical Event Trigger Identification As a basic task of natural language processing, the performance of biomedical event trigger identification is lower than that of named entity recognition(NER), which is mainly limited by complex events and the scale of annotated corpus. In recent years, researches on the identification of biomedical event trigger mainly focus on the small size of corpus, and tend to use external knowledge or joint training to improve the final performance. However, there are few researches on the insufficient mining of the inherent features of texts caused by complex events. This article focuses on biomedical event trigger identification in text articles using a neural network model that combines bidirectional gated Recurrent Unit with a selfattention mechanism. In this method, contextrelated information is captured by bidirectional gated recurrent unit to better understand and express sentence structure, selfattention mechanism is adopted to fully explore the features of text itself, and finally to improve the performance of trigger identification of complex events. A series of comparative experiments were carried out on MLEE corpus, and the experimental results verified the effectiveness of the method.

An Improved Biomedical Event Trigger Identification Framework via Modeling Document with Hierarchical Attention Biomedical event extraction has wide applications in biomedicine field. As a prerequisite step in biomedical event extraction, event trigger identification has attracted growing attention in biomedical research. Although many approaches have been proposed for biomedical event trigger identification, two main challenges still remain for researchers: 1) most of the existing approaches treat each sentence separately in biomedical documents, failing to make full use of the semantics in the global document context; 2) the sparseness of event triggers leads to a serious issue of imbalanced class for trigger identification. In this paper, we propose an endtoend framework for biomedical event trigger identification which addresses effectively the two mentioned challenges accordingly. Specifically, a hierarchical attention mechanism is used to model the global document context, including the semantic relationships both among words in the same sentence and among sentences in the same document. In addition, an adaptive class weight learning method is proposed to treat the class imbalance issue in biomedical event trigger identification. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed framework.