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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\u00a0=\u00a063.25% and F1-Measure\u00a0= 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\u00a0=\u00a072.63% and F1-Measure = 0.60) are low in comparison to previously reported for the Urdu Lexical Sample task.<\/jats:p>","DOI":"10.1145\/3477578","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:44:30Z","timestamp":1635727470000},"page":"1-16","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Investigating the Feasibility of Deep Learning Methods for Urdu Word Sense Disambiguation"],"prefix":"10.1145","volume":"21","author":[{"given":"Ali","family":"Saeed","sequence":"first","affiliation":[{"name":"Department of Software Engineering, The University of Lahore, and Department of Computer Sciences, COMSATS University Islamabad, Lahore, Punjab, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rao Muhammad Adeel","family":"Nawab","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, COMSATS UniversityIslamabad, Lahore Campus, Lahore, Punjab, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Stevenson","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, University of Sheffield, Western Bank, Sheffield, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-017-0918-0"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.5555\/1621969.1621991"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/fi10120123"},{"key":"e_1_3_2_5_2","article-title":"Corpus specificity in LSA and Word2vec: The role of out-of-domain documents","author":"Altszyler Edgar","year":"2017","unstructured":"Edgar Altszyler, Mariano Sigman, and Diego Fern\u00e1ndez Slezak. 2017. 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