TWE‐WSD: An effective topical word embedding based word sense disambiguation

Lianyin Jia, Jilin Tang, Mengjuan Li, Jinguo You, Jiaman Ding, Yinong Chen
2021 CAAI Transactions on Intelligence Technology  
Word embedding has been widely used in word sense disambiguation (WSD) and many other tasks in recent years for it can well represent the semantics of words. However, the existing word embedding methods mostly represent each word as a single vector, without considering the homonymy and polysemy of the word; thus, their performances are limited. In order to address this problem, an effective topical word embedding (TWE)-based WSD method, named TWE-WSD, is proposed, which integrates Latent
more » ... et Allocation (LDA) and word embedding. Instead of generating a single word vector (WV) for each word, TWE-WSD generates a topical WV for each word under each topic. Effective integrating strategies are designed to obtain high quality contextual vectors. Extensive experiments on SemEval-2013 and SemEval-2015 for English all-words tasks showed that TWE-WSD outperforms other state-of-the-art WSD methods, especially on nouns. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
doi:10.1049/cit2.12006 fatcat:zzbdxvi355c3bpt3zvukgzyjru