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- Title
Word Sense Disambiguation Combining Knowledge Graph and Text Hierarchical.
- Authors
CAO Yukun; JIN Chengkun; TANG Yijia; WEI Ziyue; LI Yunfeng
- Abstract
The current supervised word sense disambiguation model that utilizes annotated information with different word sense and pre-trained language models has achieved high disambiguation results. However, the supervised word sense disambiguation models are less scalable due to the difficulty of obtaining semantic data for manual annotation. The article proposes a bi-encoder word sense disambiguation method combining knowledge graph and text hierarchy, by introducing structured knowledge in 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 outperforms the comparison algorithms and achieves better results.
- Subjects
KNOWLEDGE graphs; LANGUAGE models; SEMANTICS; VOCABULARY
- Publication
Journal of Computer Engineering & Applications, 2023, Vol 59, Issue 14, p158
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2205-0400