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Integrating techniques of social network analysis and word embedding for word sense disambiguation

Purpose This research addresses the challenge of polysemous words in word embedding techniques, which are commonly used in text mining. It aims to resolve word sense ambiguity by introducing a social network sense disambiguation (SNSD) model based on social network analysis (SNA). Design/methodology/approach The SNSD model treats words as members of a social network and their co-occurrence relationships as interactions. By analyzing these interactions, the model identifies words with high betweenness centrality, which may act as bridges between different word sense communities, indicating polysemy. This unsupervised method does not rely on pre-tagged resources and is validated using the IMDb dataset. Findings The SNSD model effectively resolves word sense ambiguity in word embeddings, proving to be a cost-effective and adaptable solution …

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