SensPick: Sense Picking for Word Sense Disambiguation

• URL: http://arxiv.org/abs/2102.05260v1
• Date: Wed, 10 Feb 2021 04:52:42 GMT
• Title: SensPick: Sense Picking for Word Sense Disambiguation
• Authors: Sm Zobaed, Md Enamul Haque, Md Fazle Rabby, and Mohsen Amini Salehi
• Abstract summary: We use both context and related gloss information of a target word to model the semantic relationship between the word and the set of glosses. We propose SensPick, a type of stacked bidirectional Long Short Term Memory (LSTM) network to perform the WSD task.
• Score: 1.1429576742016154
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Word sense disambiguation (WSD) methods identify the most suitable meaning of a word with respect to the usage of that word in a specific context. Neural network-based WSD approaches rely on a sense-annotated corpus since they do not utilize lexical resources. In this study, we utilize both context and related gloss information of a target word to model the semantic relationship between the word and the set of glosses. We propose SensPick, a type of stacked bidirectional Long Short Term Memory (LSTM) network to perform the WSD task. The experimental evaluation demonstrates that SensPick outperforms traditional and state-of-the-art models on most of the benchmark datasets with a relative improvement of 3.5% in F-1 score. While the improvement is not significant, incorporating semantic relationships brings SensPick in the leading position compared to others.

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