{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T10:12:20Z","timestamp":1777975940638,"version":"3.51.4"},"reference-count":135,"publisher":"Association for Computing Machinery (ACM)","issue":"9","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Higher Education Commission Pakistan","award":["NRPU-20-16560"],"award-info":[{"award-number":["NRPU-20-16560"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>Exponential growth of electronic data requires advanced multi-label classification approaches for the development of natural language processing (NLP) applications such as recommendation systems, drug reaction detection, hate speech detection, and opinion recognition\/mining. To date, several machine and deep learning\u2013based multi-label classification methodologies have been proposed for English, French, German, Chinese, Arabic, and other developed languages. Urdu is the 11th largest language in the world and has no computer-aided multi-label textual news classification approach. Unlike other languages, Urdu is lacking multi-label text classification datasets that can be used to benchmark the performance of existing machine and deep learning methodologies. With an aim to accelerate and expedite research for the development of Urdu multi-label text classification\u2013based applications, this article provides multiple contributions as follows: First, it provides a manually annotated multi-label textual news classification dataset for the Urdu language. Second, it benchmarks the performance of traditional machine learning approaches particularly by adapting three data transformation approaches along with three top-performing machine learning classifiers and four algorithm adaptation-based approaches. Third, it benchmarks performance of 16 existing deep learning approaches and the four most widely used language models. Finally, it provides an ensemble approach that reaps the benefits of three different deep learning architectures to precisely predict different classes associated with a particular Urdu textual document. Experimental results reveal that proposed ensemble approach performance values (87% accuracy, 92% F1-score, and 8% hamming loss) are significantly higher than adapted machine and deep learning\u2013based approaches.<\/jats:p>","DOI":"10.1145\/3616111","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T11:46:29Z","timestamp":1693223189000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["EnML: Multi-label Ensemble Learning for Urdu Text Classification"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3222-7424","authenticated-orcid":false,"given":"Faiza","family":"Mehmood","sequence":"first","affiliation":[{"name":"Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology, Lahore, Pakistan and Department of Computer Science, University of Engineering and Technology, Lahore, (Faisalabad Campus) Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2865-7599","authenticated-orcid":false,"given":"Rehab","family":"Shahzadi","sequence":"additional","affiliation":[{"name":"Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology, Lahore, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5097-5728","authenticated-orcid":false,"given":"Hina","family":"Ghafoor","sequence":"additional","affiliation":[{"name":"Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology, Lahore, Pakistan; German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany and Department of Computer Science, Rheinland Pf\u00e4lzische Technische Universit\u00e4t, Kaiserslautern, 67663, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5507-198X","authenticated-orcid":false,"given":"Muhammad Nabeel","family":"Asim","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6733-2569","authenticated-orcid":false,"given":"Muhammad Usman","family":"Ghani","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6471-8420","authenticated-orcid":false,"given":"Waqar","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology, Lahore, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-8255","authenticated-orcid":false,"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany and Department of Computer Science, Rheinland Pf\u00e4lzische Technische Universit\u00e4t, Kaiserslautern, 67663, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"17478","DOI":"10.1038\/s41598-022-22523-3","article-title":"Deep learning for religious and continent-based toxic content detection and classification","volume":"12","author":"Abbasi Ahmed","year":"2022","unstructured":"Ahmed Abbasi, Abdul Rehman Javed, Farkhund Iqbal, Natalia Kryvinska, and Zunera Jalil. 2022. 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