<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Neural Network Models for Word Sense Disambiguation: An Overview

Institute of Information and Communication Technologies, Bulgarian Academy of Sciences

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Neural Network Models for Word Sense Disambiguation: An Overview"}

The following article presents an overview of the use of artificial neural networks for the task of Word Sense Disambiguation (WSD). More specifically, it...

Neural Network Models for Word Sense Disambiguation: An Overview

Online veröffentlicht: 30. März 2018

Seitenbereich: 139 - 151

Eingereicht: 01. Juli 2017

Akzeptiert: 23. Okt. 2017

DOI: https://doi.org/10.2478/cait-2018-0012

Schlüsselwörter
Word sense disambiguation, neural networks, long short-term memory cells, word embeddings, sense embeddings, context representation

© 2018 Alexander Popov, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Neural Network Models for Word Sense Disambiguation: An Overview

Online veröffentlicht: 30. März 2018

Seitenbereich: 139 - 151

Eingereicht: 01. Juli 2017

Akzeptiert: 23. Okt. 2017

DOI: https://doi.org/10.2478/cait-2018-0012

SchlüsselwörterWord sense disambiguation, neural networks, long short-term memory cells, word embeddings, sense embeddings, context representation

© 2018 Alexander Popov, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Word sense disambiguation, neural networks, long short-term memory cells, word embeddings, sense embeddings, context representation