Saeed, A., Nawab, R.M.A., Stevenson, R. orcid.org/0000-0002-9483-6006 et al. (1 more author) (2019) A sense annotated corpus for All-Words Urdu Word Sense Disambiguation. ACM Transactions on Asian and Low-Resource Language Information Processing, 18 (4). 40. ISSN 2375-4699
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-grams are applied to the corpus and the best performance (accuracy of 57.71%) is achieved using word 4-grams. The corpus is freely available to the research community to encourage further WSD research in Urdu.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 ACM. This is an author-produced version of a paper subsequently published in ACM Transactions on Asian and Low-Resource Language Information Processing. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Word Sense Disambiguation; All-Words Task; Sense Tagged Urdu Corpus |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Feb 2019 12:12 |
Last Modified: | 04 Jun 2019 11:14 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3314940 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142835 |