The application of bidirectional gated recurrent unit neural network in biomedical event trigger identification

Zifan Meng

doi:10.1117/12.2624896

13 December 2021 The application of bidirectional gated recurrent unit neural network in biomedical event trigger identification

Zifan Meng

Author Affiliations +

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120870Y (2021) https://doi.org/10.1117/12.2624896
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

Biomedical event extraction aims to extract structured biomedical events from unstructured mass biomedical literature, describing fine-grained biomedical relationships between events and biological entities. Biomedical event extraction reduces human effort and provides support for constructing a relevant database of disease diagnosis. Trigger word and argument are two components of a biomedical event. The trigger word refers to the word or phrase that triggers the event, and its type determines the type of the event. In contrast, argument refers to the event's participant, which can be a biological entity or another event. The current biomedical event extraction system uses the phased approach, with trigger word identification being the first step of the phased method. In this paper, an extended version of the recurrent neural network, i.e., the bidirectional gated recurrent unit (Bi-GRU) neural network, is utilized, to conduct the biomedical event trigger identification task. Specifically, the inputted word sequence tensor first passes through an embedding module including a word embedding layer and an entity-type label embedding layer to obtain the concatenated token representations for each sentence. Then, the token representations are fed into a Bi-GRU module to acquire the contextual encoding, which is used to conduct the trigger word identification task. The experiment is based on the MLEE dataset, a commonly used biomedical event extraction dataset. The experiment result shows that the proposed model can achieve some comparable performances with Precision 79.62%, Recall 78.64%, and F-score 78.82%

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Zifan Meng "The application of bidirectional gated recurrent unit neural network in biomedical event trigger identification", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120870Y (13 December 2021); https://doi.org/10.1117/12.2624896

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KEYWORDS

Biomedical optics

Neural networks

Computer programming

Associative arrays

Machine learning

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Performance modeling

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