Comparative Analysis of Recurrent Neural Network Architectures for Arabic Word Sense Disambiguation

Rakia Saidi, Fethi Jarray, Mohammed Alsuhaibani

2022

Abstract

Word Sense Disambiguation (WSD) refers to the process of discovering the correct sense of an ambiguous word occurring in a given context. In this paper, we address the problem of Word Sense Disambiguation of low-resource languages such as Arabic language. We model the problem as a supervised sequence-to-sequence learning where the input is a stream of tokens and the output is the sequence of senses for the ambiguous words. We propose four recurrent neural network (RNN) architectures including Vanilla RNN, LSTM, BiLSTM and GRU. We achieve, respectively, 85.22%, 88.54%, 90.77% and 92.83% accuracy with Vanilla RNN, LSTM, BiLSTM and GRU. The obtained results demonstrate superiority of GRU based deep learning Model for WSD over the existing RNN models.

Paper Citation

in Harvard Style

Saidi R., Jarray F. and Alsuhaibani M. (2022). Comparative Analysis of Recurrent Neural Network Architectures for Arabic Word Sense Disambiguation. In Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-613-2, pages 272-277. DOI: 10.5220/0011527600003318

in Bibtex Style

@conference{webist22,
author={Rakia Saidi and Fethi Jarray and Mohammed Alsuhaibani},
title={Comparative Analysis of Recurrent Neural Network Architectures for Arabic Word Sense Disambiguation},
booktitle={Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2022},
pages={272-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011527600003318},
isbn={978-989-758-613-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Comparative Analysis of Recurrent Neural Network Architectures for Arabic Word Sense Disambiguation
SN - 978-989-758-613-2
AU - Saidi R.
AU - Jarray F.
AU - Alsuhaibani M.
PY - 2022
SP - 272
EP - 277
DO - 10.5220/0011527600003318