Attention Neural Network for Biomedical Word Sense Disambiguation

Chun-Xiang Zhang, Shu-Yang Pang, Xue-Yao Gao, Jia-Qi Lu, Bo Yu and Ya Jia

Discrete Dynamics in Nature and Society, 2022, vol. 2022, 1-14

Abstract: In order to improve the disambiguation accuracy of biomedical words, this paper proposes a disambiguation method based on the attention neural network. The biomedical word is viewed as the center. Morphology, part of speech, and semantic information from 4 adjacent lexical units are extracted as disambiguation features. The attention layer is used to generate a feature matrix. Average asymmetric convolutional neural networks (Av-ACNN) and bidirectional long short-term memory (Bi-LSTM) networks are utilized to extract features. The softmax function is applied to determine the semantic category of the biomedical word. At the same time, CNN, LSTM, and Bi-LSTM are applied to biomedical WSD. MSH corpus is adopted to optimize CNN, LSTM, Bi-LSTM, and the proposed method and testify their disambiguation performance. Experimental results show that the average disambiguation accuracy of the proposed method is improved compared with CNN, LSTM, and Bi-LSTM. The average disambiguation accuracy of the proposed method achieves 91.38%.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:6182058

DOI: 10.1155/2022/6182058

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