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A Sense Annotated Corpus for All-Words Urdu Word Sense Disambiguation

Authors:

Rao Muhammad Adeel Nawab,

Mark Stevenson,

Paul RaysonAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 18, Issue 4

Article No.: 40, Pages 1 - 14

https://doi.org/10.1145/3314940

Published: 07 May 2019 Publication History

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Abstract

Word Sense Disambiguation (WSD) aims to automatically predict the correct sense of a word used in a given context. All human languages exhibit word sense ambiguity, and resolving this ambiguity can be difficult. Standard benchmark resources are required to develop, compare, and evaluate WSD techniques. These are available for many languages, but not for Urdu, despite this being a language with more than 300 million speakers and large volumes of text available digitally. To fill this gap, this study proposes a novel benchmark corpus for the Urdu All-Words WSD task. The corpus contains 5,042 words of Urdu running text in which all ambiguous words (856 instances) are manually tagged with senses from the Urdu Lughat dictionary. A range of baseline WSD models based on n-gram are applied to the corpus, and the best performance (accuracy of 57.71%) is achieved using word 4-gram. The corpus is freely available to the research community to encourage further WSD research in Urdu.

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Index Terms

A Sense Annotated Corpus for All-Words Urdu Word Sense Disambiguation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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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 18, Issue 4

December 2019

305 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3327969

Editor:
Nianwen Xue
Brandeis University, Waltham, USA

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Copyright © 2019 ACM.

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Publication History

Published: 07 May 2019

Accepted: 01 February 2019

Revised: 01 November 2018

Received: 01 August 2018

Published in TALLIP Volume 18, Issue 4

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Cited By

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https://doi.org/10.14201/adcaij.31084
Shafi JAdeel Nawab RRayson P(2023)Semantic Tagging for the Urdu Language: Annotated Corpus and Multi-Target Classification MethodsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358249622:6(1-32)Online publication date: 16-Feb-2023
https://dl.acm.org/doi/10.1145/3582496
Baer MPurves R(2023)Identifying Landscape Relevant Natural Language using Actively Crowdsourced Landscape Descriptions and Sentence-TransformersKI - Künstliche Intelligenz10.1007/s13218-022-00793-337:1(55-67)Online publication date: 20-Jan-2023
https://doi.org/10.1007/s13218-022-00793-3
Mir TLawaye AAhmed GRana P(2023)Advances Toward Word-Sense DisambiguationInternational Conference on Innovative Computing and Communications10.1007/978-981-99-4071-4_15(177-189)Online publication date: 26-Oct-2023
https://doi.org/10.1007/978-981-99-4071-4_15
Malkawi RDaradkeh MEl-Hassan APetrov P(2022)A Semantic Similarity-Based Identification Method for Implicit Citation Functions and Sentiments InformationInformation10.3390/info1311054613:11(546)Online publication date: 17-Nov-2022
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Humayoun MAkhtar N(2022)CORPURES: Benchmark corpus for urdu extractive summaries and experiments using supervised learningIntelligent Systems with Applications10.1016/j.iswa.2022.20012916(200129)Online publication date: Nov-2022
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A. Rana TBakht BAfzal MAli Mian NWaseem Iqbal MKhalid ARaza Naqvi M(2021)Extraction of Opinion Target Using Syntactic Rules in Urdu TextIntelligent Automation & Soft Computing10.32604/iasc.2021.01857229:3(839-853)Online publication date: 2021
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Saeed ANawab RStevenson M(2021)Investigating the Feasibility of Deep Learning Methods for Urdu Word Sense DisambiguationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/347757821:2(1-16)Online publication date: 31-Oct-2021
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