Neural Network Models for Word Sense Disambiguation: An Overview
2018 ◽
Vol 18
(1)
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pp. 139-151
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Abstract The following article presents an overview of the use of artificial neural networks for the task of Word Sense Disambiguation (WSD). More specifically, it surveys the advances in neural language models in recent years that have resulted in methods for the effective distributed representation of linguistic units. Such representations – word embeddings, context embeddings, sense embeddings – can be effectively applied for WSD purposes, as they encode rich semantic information, especially in conjunction with recurrent neural networks, which are able to capture long-distance relations encoded in word order, syntax, information structuring.
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2020 ◽
Vol 34
(05)
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pp. 8139-8146
2017 ◽
Vol 73
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pp. 137-147
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