SensPick: Sense Picking for Word Sense Disambiguation
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.
2021 IEEE 15th International Conference on Semantic Computing (ICSC)