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Investigating the Feasibility of Deep Learning Methods for Urdu Word Sense Disambiguation

Authors:

Rao Muhammad Adeel Nawab,

Mark StevensonAuthors Info & Claims

Transactions on Asian and Low-Resource Language Information Processing, Volume 21, Issue 2

Article No.: 38, Pages 1 - 16

https://doi.org/10.1145/3477578

Published: 31 October 2021 Publication History

Abstract

Word Sense Disambiguation (WSD), the process of automatically identifying the correct meaning of a word used in a given context, is a significant challenge in Natural Language Processing. A range of approaches to the problem has been explored by the research community. The majority of these efforts has focused on a relatively small set of languages, particularly English. Research on WSD for South Asian languages, particularly Urdu, is still in its infancy. In recent years, deep learning methods have proved to be extremely successful for a range of Natural Language Processing tasks. The main aim of this study is to apply, evaluate, and compare a range of deep learning methods approaches to Urdu WSD (both Lexical Sample and All-Words) including Simple Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Units, Bidirectional Long-Short Term Memory, and Ensemble Learning. The evaluation was carried out on two benchmark corpora: (1) the ULS-WSD-18 corpus and (2) the UAW-WSD-18 corpus. Results (Accuracy = 63.25% and F1-Measure = 0.49) show that a deep learning approach outperforms previously reported results for the Urdu All-Words WSD task, whereas performance using deep learning approaches (Accuracy = 72.63% and F1-Measure = 0.60) are low in comparison to previously reported for the Urdu Lexical Sample task.

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Cited By

Habtamu RGizachew B(2024)State-of-the-Art Approaches to Word Sense Disambiguation: A Multilingual InvestigationPan-African Conference on Artificial Intelligence10.1007/978-3-031-57624-9_10(176-202)Online publication date: 7-Apr-2024
https://doi.org/10.1007/978-3-031-57624-9_10
Farhat Ullah MSaeed AHussain N(2023)Comparison of Pre-trained vs Custom-trained Word Embedding Models for Word Sense DisambiguationADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal10.14201/adcaij.3108412:1(e31084)Online publication date: 1-Nov-2023
https://doi.org/10.14201/adcaij.31084

Index Terms

Investigating the Feasibility of Deep Learning Methods for Urdu Word Sense Disambiguation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 21, Issue 2

March 2022

413 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3494070

Editor:
Imed Zitouni
Google, USA

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Publication History

Published: 31 October 2021

Accepted: 01 July 2021

Revised: 01 May 2021

Received: 01 September 2019

Published in TALLIP Volume 21, Issue 2

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Cited By

Habtamu RGizachew B(2024)State-of-the-Art Approaches to Word Sense Disambiguation: A Multilingual InvestigationPan-African Conference on Artificial Intelligence10.1007/978-3-031-57624-9_10(176-202)Online publication date: 7-Apr-2024
https://doi.org/10.1007/978-3-031-57624-9_10
Farhat Ullah MSaeed AHussain N(2023)Comparison of Pre-trained vs Custom-trained Word Embedding Models for Word Sense DisambiguationADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal10.14201/adcaij.3108412:1(e31084)Online publication date: 1-Nov-2023
https://doi.org/10.14201/adcaij.31084

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