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

Ali Saeed, Rao Muhammad Adeel Nawab, Mark Stevenson, Paul Rayson
2019 ACM Transactions on Asian and Low-Resource Language Information Processing  
PAUL RAYSON, Lancaster University, UK 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 diicult. 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
more » ... itally. To ill 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.
doi:10.1145/3314940 fatcat:awjrtihv6vew7eusjakt4snxjy