陈俊帆
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陈俊帆
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论文
Word Sense Disambiguation by Refining Target Word Embedding
发布时间：2025-10-22点击次数：
发表刊物：
Proceedings of the ACM Web Conference 2023 (WWW), CCF-A
摘要：
Word Sense Disambiguation (WSD) which aims to identify the correct sense of a target word appearing in a specific context is essential for web text analysis. The use of glosses has been explored as a means for WSD. However, only a few works model the correlation between the target context and gloss. We add to the body of literature by presenting a model that employs a multi-head attention mechanism on deep contextual features of the target word and candidate glosses to refine the target word embedding. Furthermore, to encourage the model to learn the relevant part of target features that align with the correct gloss, we recursively alternate attention on target word features and that of candidate glosses to gradually extract the relevant contextual features of the target word, refining its representation and strengthening the final disambiguation results. Empirical studies on the five most commonly used benchmark datasets show that our proposed model is effective and achieves state-of-the-art results.
合写作者：
Xuefeng Zhang,张日崇, Xiaoyang Li, Fanshuang Kong,陈俊帆, Samuel Mensah
论文类型：
国际学术会议
页面范围：
1405-1414
是否译文：
否
发表时间：
2023-01-01