scholarly journals Toward Universal Word Sense Disambiguation Using Deep Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 60264-60275 ◽  
Author(s):  
Hiram Calvo ◽  
Arturo P. Rocha-Ramirez ◽  
Marco A. Moreno-Armendariz ◽  
Carlos A. Duchanoy
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation

scholarly journals Multi-Task Learning for Metaphor Detection with Graph Convolutional Neural Networks and Word Sense Disambiguation

2020 ◽  
Vol 34 (05) ◽  
pp. 8139-8146
Author(s):  
Duong Le ◽  
My Thai ◽  
Thien Nguyen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Knowledge Resources ◽  
Useful Knowledge ◽  
Learning Framework ◽  
Task Learning ◽  
Sense Disambiguation

The current deep learning works on metaphor detection have only considered this task independently, ignoring the useful knowledge from the related tasks and knowledge resources. In this work, we introduce two novel mechanisms to improve the performance of the deep learning models for metaphor detection. The first mechanism employs graph convolutional neural networks (GCN) with dependency parse trees to directly connect the words of interest with their important context words for metaphor detection. The GCN networks in this work also present a novel control mechanism to filter the learned representation vectors to retain the most important information for metaphor detection. The second mechanism, on the other hand, features a multi-task learning framework that exploits the similarity between word sense disambiguation and metaphor detection to transfer the knowledge between the two tasks. The extensive experiments demonstrate the effectiveness of the proposed techniques, yielding the state-of-the-art performance over several datasets.

Evolving Neural Networks for Word Sense Disambiguation

2008 ◽  
Author(s):  
A. Azzini ◽  
C. da Costa Pereira ◽  
M. Dragoni ◽  
A.G.B. Tettamanzi
Keyword(s):  
Neural Networks ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation

scholarly journals Capsule Network Improved Multi-Head Attention for Word Sense Disambiguation

2021 ◽  
Vol 11 (6) ◽  
pp. 2488
Author(s):  
Jinfeng Cheng ◽  
Weiqin Tong ◽  
Weian Yan
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Semantic Representation ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Word Sense ◽  
Great Contribution ◽  
Specific Context ◽  
The Core ◽  
Sense Disambiguation

Word sense disambiguation (WSD) is one of the core problems in natural language processing (NLP), which is to map an ambiguous word to its correct meaning in a specific context. There has been a lively interest in incorporating sense definition (gloss) into neural networks in recent studies, which makes great contribution to improving the performance of WSD. However, disambiguating polysemes of rare senses is still hard. In this paper, while taking gloss into consideration, we further improve the performance of the WSD system from the perspective of semantic representation. We encode the context and sense glosses of the target polysemy independently using encoders with the same structure. To obtain a better presentation in each encoder, we leverage the capsule network to capture different important information contained in multi-head attention. We finally choose the gloss representation closest to the context representation of the target word as its correct sense. We do experiments on English all-words WSD task. Experimental results show that our method achieves good performance, especially having an inspiring effect on disambiguating words of rare senses.

scholarly journals Neural Network Models for Word Sense Disambiguation: An Overview

2018 ◽  
Vol 18 (1) ◽  
pp. 139-151 ◽  
Author(s):  
Alexander Popov
Keyword(s):  
Neural Networks ◽  
Semantic Information ◽  
Word Sense Disambiguation ◽  
Network Models ◽  
Language Models ◽  
Word Sense ◽  
Neural Network Models ◽  
Long Distance ◽  
Sense Disambiguation ◽  
Linguistic Units

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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