Biomedical Word Sense Disambiguation Based on Graph Attention Networks

Abstract

Biomedical words have many semantics. Biomedical word sense disambiguation (WSD) is an important research issue in biomedicine field. Biomedical WSD refers to the process of determining meanings of ambiguous word according to its context. It is widely applied to process, translate and retrieve biomedical texts now. In order to improve WSD accuracy in biomedicine, this paper proposes a new WSD method based on graph attention neural network (GAT). Words, parts of speech, and semantic categories in context of ambiguous word are used as disambiguation features. Disambiguation features and the sentence are used as nodes to construct WSD graph. GAT is used to extract discriminative features, and softmax function is applied to determine semantic category of biomedical ambiguous word. MSH dataset is used to optimize GAT-based WSD classifier and test its accuracy. Experiments show that average accuracy of the proposed method is improved. At the same time, majority voting strategy is adopted to optimize GAT-based WSD classifier further.