Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation

IEEE Access

Yoonseok Heo¹  Jungyun Seo¹  Sangwoo Kang² 

[1] Department of Computer Science and Engineering, Sogang University, Seoul, South Korea;Department of Software, Gachon University, Gyeonggi, South Korea;

关键词: Computational and artificial intelligence;    English vocabulary learning;    natural language processing;    neural networks;    word sense disambiguation;   

DOI  :  10.1109/ACCESS.2020.2970436

来源: DOAJ

【 摘 要 】

Word sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a particular context. Although recent studies have demonstrated some progress in the advancement of neural language models, the scope of research is still such that the senses of several words can only be determined in a few domains. Therefore, it is necessary to move toward developing a highly scalable process that can address a lot of senses occurring in various domains. This paper introduces a new large WSD dataset that is automatically constructed from the Oxford Dictionary, which is widely used as a standard source for the meaning of words. We propose a new WSD model that individually determines the sense of the word in accordance with its part of speech in the context. In addition, we introduce a hybrid sense prediction method that separately classifies the less frequently used senses for achieving a reasonable performance. We have conducted comparative experiments to demonstrate that the proposed method is more reliable compared with the baseline approaches. Also, we investigated the adaptation of the method to a realistic environment with the use of news articles.

【 授权许可】

Unknown