Word Sense Disambiguation Combining Knowledge Graph and Text Hierarchical Structure

Current supervised word sense disambiguation models have obtained high disambiguation results using annotated information of different word senses and pre-trained language models. However, the semantic data of the supervised word sense disambiguation models are in the form of short texts, and much of the corpus information is not rich enough to distinguish the semantics in different scenarios. This article proposes a bi-encoder word sense disambiguation method combining a knowledge graph and text hierarchy structure, by introducing structured knowledge from the knowledge graph to supplement more extended semantic information, using the hierarchy of contextual input text to describe the meaning of words and phrases, and constructing a BERT-based bi-encoder, introducing a graph attention network to reduce the noise information in the contextual input text, so as to improve the disambiguation accuracy of the target words in phrase form and ultimately improve the disambiguation effectiveness of the method. By comparing the method with the latest nine comparison algorithms in five test datasets, the disambiguation accuracy of the method mostly outperformed the comparison algorithms and achieved better results.