Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks
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METHODOLOGY ARTICLE
Open Access
Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks Lvxing Zhu1
and Haoran Zheng1,2,3*
Abstract Background: Biomedical event extraction is a fundamental and in-demand technology that has attracted substantial interest from many researchers. Previous works have heavily relied on manual designed features and external NLP packages in which the feature engineering is large and complex. Additionally, most of the existing works use the pipeline process that breaks down a task into simple sub-tasks but ignores the interaction between them. To overcome these limitations, we propose a novel event combination strategy based on hybrid deep neural networks to settle the task in a joint end-to-end manner. Results: We adapted our method to several annotated corpora of biomedical event extraction tasks. Our method achieved state-of-the-art performance with noticeable overall F1 score improvement compared to that of existing methods for all of these corpora. Conclusions: The experimental results demonstrated that our method is effective for biomedical event extraction. The combination strategy can reconstruct complex events from the output of deep neural networks, while the deep neural networks effectively capture the feature representation from the raw text. The biomedical event extraction implementation is available online at http://www.predictor.xin/event_extraction. Keywords: Event extraction, Biomedical text, Deep learning Neural network
Background PubMed recorded over 28 million papers in 2018 [1] which reflects the rapid growth of the biomedical literature. The knowledge and discoveries reported in the biomedical literature receive substantial attention, but the large volume of the literature poses a challenge to information retrieval; therefore, text mining has become an in-demand technology and a popular research focus. Event extraction, which is an effective way to represent the structured knowledge from unstructured text [2], is a fundamental technology for text mining. However, event extraction is particularly difficult due to the complex and *Correspondence: [email protected];[email protected] School of Computer Science and Technology, University of Science and Technology of China, Huangshan Road, 230026 Hefei, People’s Republic of China 2 Anhui Key Laboratory of Software Engineering in Computing and Communication, University of Science and Technology of China, Huangshan Road, 230026 Hefei, People’s Republic of China Full list of author information is available at the end of the article 1
arbitrary structure of events in biomedicine, so related research is urgently needed [3]. The definition of a biomedical event, according to the BioNLP [4], consists of (1) a trigger word that indicates the existence of an event and belongs to a certain event type and (2) multiple arguments in which an argument can be viewed as a relation between the event triggers and entities or other event, and each argument has an argument type as well. Th
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