{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T06:40:24Z","timestamp":1776840024456,"version":"3.51.2"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>\n            Continuous proliferation of hate speech in different languages on social media has drawn significant attention from researchers in the past decade. Detecting hate speech is indispensable irrespective of the scale of use of language, as it inflicts huge harm on society. This work presents a first resource for classifying the severity of hate speech in addition to classifying offensive and hate speech content. Current research mostly limits hate speech classification to its primary categories, such as racism, sexism, and hatred of religions. However, hate speech targeted at different protected characteristics also manifests in different forms and intensities. It is important to understand varying severity levels of hate speech so that the most harmful cases of hate speech may be identified and dealt with earlier than the less harmful ones. In this work, we focus on detecting offensive speech, hate speech, and multiple levels of hate speech in the Urdu language. We investigate three primary target categories of hate speech: religion, racism, and national origin. We further divide these categories into levels based on the severity of hate conveyed. The severity levels are referred to as\n            <jats:italic>symbolization<\/jats:italic>\n            ,\n            <jats:italic>insult<\/jats:italic>\n            , and\n            <jats:italic>attribution<\/jats:italic>\n            . A corpus comprising more than 20,000 tweets against the corresponding hate speech categories and severity levels is collected and annotated. A comprehensive experimentation scheme is applied using traditional as well as deep learning\u2013based models to examine their impact on hate speech detection. The highest macro-averaged F-score yielded for detecting offensive speech is 86% while the highest F-scores for detecting hate speech with respect to ethnicity, national origin, and religious affiliation are 80%, 81%, and 72%, respectively. This shows that results are very encouraging and would provide a lead towards further investigation in this domain.\n          <\/jats:p>","DOI":"10.1145\/3580476","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T13:24:07Z","timestamp":1674134647000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Detection of Offensive Language and ITS Severity for Low Resource Language"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9504-0368","authenticated-orcid":false,"given":"Ramsha","family":"Saeed","sequence":"first","affiliation":[{"name":"National University of Science and Technology (NUST), Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9583-5585","authenticated-orcid":false,"given":"Hammad","family":"Afzal","sequence":"additional","affiliation":[{"name":"National University of Science and Technology (NUST), Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0400-3869","authenticated-orcid":false,"given":"Sadaf Abdul","family":"Rauf","sequence":"additional","affiliation":[{"name":"Fatima Jinnah Women University (FJWU), Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5392-5187","authenticated-orcid":false,"given":"Naima","family":"Iltaf","sequence":"additional","affiliation":[{"name":"National University of Science and Technology (NUST), Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/EISIC.2016.032","volume-title":"2016 European Intelligence and Security Informatics Conference (EISIC\u201916)","author":"Agarwal Swati","year":"2016","unstructured":"Swati Agarwal and Ashish Sureka. 2016. But I did not mean it! Intent classification of racist posts on Tumblr. In 2016 European Intelligence and Security Informatics Conference (EISIC\u201916). IEEE, 124\u2013127."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2994950"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/1690299.1690305"},{"key":"e_1_3_2_5_2","first-page":"69","volume-title":"2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM\u201918)","author":"Albadi Nuha","year":"2018","unstructured":"Nuha Albadi, Maram Kurdi, and Shivakant Mishra. 2018. Are they our brothers? Analysis and detection of religious hate speech in the Arabic twittersphere. In 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM\u201918). IEEE, 69\u201376."},{"key":"e_1_3_2_6_2","volume-title":"2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS\u201917)","author":"Alfina Ika","year":"2017","unstructured":"Ika Alfina, Rio Mulia, Mohamad Ivan Fanany, and Yudo Ekanata. 2017. Hate speech detection in the Indonesian language: A dataset and preliminary study. In 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS\u201917)."},{"key":"e_1_3_2_7_2","first-page":"57","volume-title":"International Conference on Applications of Natural Language to Information Systems","author":"Anzovino Maria","year":"2018","unstructured":"Maria Anzovino, Elisabetta Fersini, and Paolo Rosso. 2018. Automatic identification and classification of misogynistic language on Twitter. In International Conference on Applications of Natural Language to Information Systems. Springer, 57\u201364."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3054223"},{"key":"e_1_3_2_9_2","first-page":"1822","volume-title":"CLEF (Working Notes)","author":"Bagdon Christopher","year":"2021","unstructured":"Christopher Bagdon. 2021. Profiling spreaders of hate speech with N-grams and RoBERTa. In CLEF (Working Notes). 1822\u20131828."},{"key":"e_1_3_2_10_2","first-page":"2562","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics","author":"Zia Haris Bin","year":"2018","unstructured":"Haris Bin Zia, Agha Ali Raza, and Awais Athar. 2018. Urdu word segmentation using conditional random fields (CRFs). In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, 2562\u20132569. http:\/\/aclweb.org\/anthology\/C18-1217."},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_3_2_12_2","first-page":"1","volume-title":"EVALITA 6th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian","author":"Bosco Cristina","year":"2018","unstructured":"Cristina Bosco, Dell\u2019Orletta Felice, Fabio Poletto, Manuela Sanguinetti, and Tesconi Maurizio. 2018. Overview of the EVALITA 2018 hate speech detection task. In EVALITA 6th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, Vol. 2263. CEUR, 1\u20139."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-016-0072-6"},{"key":"e_1_3_2_14_2","article-title":"Mean Birds: Detecting aggression and bullying on Twitter","author":"Chatzakou Despoina","year":"2017","unstructured":"Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, and Athena Vakali. 2017. Mean Birds: Detecting aggression and bullying on Twitter. arXiv preprint arXiv:1702.06877 (2017).","journal-title":"arXiv preprint arXiv:1702.06877"},{"key":"e_1_3_2_15_2","first-page":"2354","volume-title":"2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI\u201915)","author":"Chavan Vikas S.","year":"2015","unstructured":"Vikas S. Chavan and S. S. Shylaja. 2015. Machine learning approach for detection of cyber-aggressive comments by peers on social media network. In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI\u201915). IEEE, 2354\u20132358."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46562-3_12"},{"key":"e_1_3_2_17_2","article-title":"Abusive and threatening language detection in Urdu using boosting based and BERT based models: A comparative approach","author":"Das Mithun","year":"2021","unstructured":"Mithun Das, Somnath Banerjee, and Punyajoy Saha. 2021. Abusive and threatening language detection in Urdu using boosting based and BERT based models: A comparative approach. arXiv preprint arXiv:2111.14830 (2021).","journal-title":"arXiv preprint arXiv:2111.14830"},{"key":"e_1_3_2_18_2","article-title":"Automated hate speech detection and the problem of offensive language","author":"Davidson Thomas","year":"2017","unstructured":"Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. Automated hate speech detection and the problem of offensive language. arXiv preprint arXiv:1703.04009 (2017).","journal-title":"arXiv preprint arXiv:1703.04009"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742760"},{"issue":"3","key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S8755-4615(01)00057-3","article-title":"Re-in\/citing linguistic injuries: Speech acts, cyberhate, and the spatial and temporal character of networked environments","volume":"18","author":"Eichhorn Kate","year":"2001","unstructured":"Kate Eichhorn. 2001. Re-in\/citing linguistic injuries: Speech acts, cyberhate, and the spatial and temporal character of networked environments. Computers and Composition 18, 3 (2001), 293\u2013304.","journal-title":"Computers and Composition"},{"key":"e_1_3_2_21_2","unstructured":"Facebook. Hate speech. (2022). Retrieved January 31 2023 from https:\/\/www.facebook.com\/communitystandards\/hate_speech."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3232676"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-3013"},{"key":"e_1_3_2_24_2","article-title":"Recognizing explicit and implicit hate speech using a weakly supervised two-path bootstrapping approach","author":"Gao Lei","year":"2017","unstructured":"Lei Gao, Alexis Kuppersmith, and Ruihong Huang. 2017. Recognizing explicit and implicit hate speech using a weakly supervised two-path bootstrapping approach. arXiv preprint arXiv:1710.07394 (2017).","journal-title":"arXiv preprint arXiv:1710.07394"},{"key":"e_1_3_2_25_2","article-title":"Peer-to-peer insult detection in online communities","author":"Goyal Priya","year":"2013","unstructured":"Priya Goyal and Gaganpreet Singh Kalra. 2013. Peer-to-peer insult detection in online communities. IITK Unpubl (2013).","journal-title":"IITK Unpubl"},{"key":"e_1_3_2_26_2","first-page":"128","volume-title":"IberEval@ SEPLN","author":"Graff Mario","year":"2018","unstructured":"Mario Graff, Sabino Miranda-Jim\u00e9nez, Eric Sadit Tellez, Daniela Moctezuma, Vladimir Salgado, Jos\u00e9 Ortiz-Bejar, and Claudia N. S\u00e1nchez. 2018. INGEOTEC at MEX-A3T: Author profiling and aggressiveness analysis in Twitter using \\(\\mu\\) TC and EvoMSA. In IberEval@ SEPLN. 128\u2013133."},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/1008992.1009074"},{"key":"e_1_3_2_28_2","article-title":"Hate speech: A study of Pakistan\u2019s cyberspace","author":"Haque Jahanzaib","year":"2014","unstructured":"Jahanzaib Haque. 2014. Hate speech: A study of Pakistan\u2019s cyberspace. Islamabad, Pakistan: Bytes4all (2014).","journal-title":"Islamabad, Pakistan: Bytes4all"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/2661126.2661133"},{"issue":"2","key":"e_1_3_2_30_2","first-page":"267","article-title":"The pragmatics of swearing","volume":"4","author":"Jay Timothy","year":"2008","unstructured":"Timothy Jay and Kristin Janschewitz. 2008. The pragmatics of swearing. Journal of Politeness Research. Language, Behaviour, Culture 4, 2 (2008), 267\u2013288.","journal-title":"Journal of Politeness Research. Language, Behaviour, Culture"},{"key":"e_1_3_2_31_2","article-title":"Code of conduct on countering illegal hate speech online: First results on implementation","author":"Jourov\u00e1 V\u011bra","year":"2016","unstructured":"V\u011bra Jourov\u00e1. 2016. Code of conduct on countering illegal hate speech online: First results on implementation. European Commission.[cit. 8. b\u0159ezen 2018] (2016).","journal-title":"European Commission.[cit. 8. b\u0159ezen 2018]"},{"key":"e_1_3_2_32_2","unstructured":"Vera Jourov\u00e1. 2016. Code of Conduct on countering illegal hate speech online: First results on implementation. Factsheet Directorate-General for Justice and Consumers."},{"key":"e_1_3_2_33_2","unstructured":"Ezgi Kan Merve Nebioglu Seyma \u00d6zkan Funda Tekin and Gamze Tosun. 2018. Media watch on hate speech report January\u2013April 2018. Hrant Dink Foundation."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3414524"},{"issue":"4","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1111\/1745-9125.12022","article-title":"High times for hate crimes: Explaining the temporal clustering of hate-motivated offending","volume":"51","author":"King Ryan D.","year":"2013","unstructured":"Ryan D. King and Gretchen M. Sutton. 2013. High times for hate crimes: Explaining the temporal clustering of hate-motivated offending. Criminology 51, 4 (2013), 871\u2013894.","journal-title":"Criminology"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"382","DOI":"10.18653\/v1\/W17-5043","volume-title":"Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications","author":"Kulmizev Artur","year":"2017","unstructured":"Artur Kulmizev, Bo Blankers, Johannes Bjerva, Malvina Nissim, Gertjan van Noord, Barbara Plank, and Martijn Wieling. 2017. The power of character n-grams in native language identification. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. 382\u2013389."},{"key":"e_1_3_2_37_2","unstructured":"Zachary Laub. 2019. Hate speech on social media: Global comparisons. (June2019). Retrieved January 31 2023 from https:\/\/www.cfr.org\/backgrounder\/hate-speech-social-media-global-comparisons."},{"key":"e_1_3_2_38_2","first-page":"1188","volume-title":"International Conference on Machine Learning","author":"Le Quoc","year":"2014","unstructured":"Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In International Conference on Machine Learning. 1188\u20131196."},{"issue":"2","key":"e_1_3_2_39_2","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1207\/S15326926CLP0602_2","article-title":"Responses to Internet hate sites: Is speech too free in cyberspace?","volume":"6","author":"Leets Laura","year":"2001","unstructured":"Laura Leets. 2001. Responses to Internet hate sites: Is speech too free in cyberspace? Communication Law & Policy 6, 2 (2001), 287\u2013317.","journal-title":"Communication Law & Policy"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368567.3368584"},{"issue":"4","key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1080\/08900520903320936","article-title":"Hate speech or \u201creasonable racism?\u201d The other in Stormfront","volume":"24","author":"Meddaugh Priscilla Marie","year":"2009","unstructured":"Priscilla Marie Meddaugh and Jack Kay. 2009. Hate speech or \u201creasonable racism?\u201d The other in Stormfront. Journal of Mass Media Ethics 24, 4 (2009), 251\u2013268.","journal-title":"Journal of Mass Media Ethics"},{"key":"e_1_3_2_42_2","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).","journal-title":"arXiv preprint arXiv:1301.3781"},{"key":"e_1_3_2_43_2","unstructured":"Bastian Birkeneder Jelena Mitrovic Julia Niemeier Leon Teubert and Siegfried Handschuh. 2018. upInf - Offensive language detection in German tweets. In Proceedings of GermEval 2018 14th Conference on Natural Language Processing (KONVENS\u201918) ."},{"key":"e_1_3_2_44_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.18653\/v1\/W17-3008","volume-title":"Proceedings of the 1st Workshop on Abusive Language Online","author":"Mubarak Hamdy","year":"2017","unstructured":"Hamdy Mubarak, Kareem Darwish, and Walid Magdy. 2017. Abusive language detection on Arabic social media. In Proceedings of the 1st Workshop on Abusive Language Online. 52\u201356."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883062"},{"issue":"1","key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1108\/S1537-466120140000018021","article-title":"Exposure to online hate among young social media users","volume":"18","author":"Oksanen Atte","year":"2014","unstructured":"Atte Oksanen, James Hawdon, Emma Holkeri, Matti N\u00e4si, and Pekka R\u00e4s\u00e4nen. 2014. Exposure to online hate among young social media users. Sociological Studies of Children & Youth 18, 1 (2014), 253\u2013273.","journal-title":"Sociological Studies of Children & Youth"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1242-y"},{"key":"e_1_3_2_48_2","first-page":"88","volume-title":"International Conference on Computing and Information Technology","author":"Putri Shofianina Dwi Ananda","year":"2021","unstructured":"Shofianina Dwi Ananda Putri, Muhammad Okky Ibrohim, and Indra Budi. 2021. Abusive language and hate speech detection for Indonesian-local language in social media text. In International Conference on Computing and Information Technology. Springer, 88\u201398."},{"issue":"1","key":"e_1_3_2_49_2","first-page":"1","article-title":"Multilingual offensive language identification for low-resource languages","volume":"21","author":"Ranasinghe Tharindu","year":"2021","unstructured":"Tharindu Ranasinghe and Marcos Zampieri. 2021. Multilingual offensive language identification for low-resource languages. Transactions on Asian and Low-Resource Language Information Processing 21, 1 (2021), 1\u201313.","journal-title":"Transactions on Asian and Low-Resource Language Information Processing"},{"key":"e_1_3_2_50_2","unstructured":"Council of Europe Committee of Ministers. 1997. Recommendation No. R (97) 20 of the Committee of Ministers to member states on \u201chate speech\u201d. (1997). Retrieved January 31 2023 from https:\/\/rm.coe.int\/1680505d5b."},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"2512","DOI":"10.18653\/v1\/2020.emnlp-main.197","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201920)","author":"Rizwan Hammad","year":"2020","unstructured":"Hammad Rizwan, Muhammad Haroon Shakeel, and Asim Karim. 2020. Hate-speech and offensive language detection in Roman Urdu. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201920). 2512\u20132522."},{"key":"e_1_3_2_52_2","first-page":"1","volume-title":"2020 IEEE 23rd International Multitopic Conference (INMIC\u201920)","author":"Sajid Tauqeer","year":"2020","unstructured":"Tauqeer Sajid, Mehdi Hassan, Mohsan Ali, and Rabia Gillani. 2020. Roman Urdu multi-class offensive text detection using hybrid features and SVM. In 2020 IEEE 23rd International Multitopic Conference (INMIC\u201920). IEEE, 1\u20135."},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.18653\/v1\/W17-3010","volume-title":"Proceedings of the 1st Workshop on Abusive Language Online","author":"Samghabadi Niloofar Safi","year":"2017","unstructured":"Niloofar Safi Samghabadi, Suraj Maharjan, Alan Sprague, Raquel Diaz-Sprague, and Thamar Solorio. 2017. Detecting nastiness in social media. In Proceedings of the 1st Workshop on Abusive Language Online. 63\u201372."},{"key":"e_1_3_2_54_2","unstructured":"Twitter. 2020. Hateful conduct policy. (2020). Retrieved January 31 2023 from https:\/\/help.twitter.com\/en\/rules-and-policies\/hateful-conduct-policy."},{"key":"e_1_3_2_55_2","first-page":"2193","volume-title":"CLEF (Working Notes)","author":"Vogel Inna","year":"2021","unstructured":"Inna Vogel and Meghana Meghana. 2021. Profiling hate speech spreaders on Twitter: SVM vs. Bi-LSTM. In CLEF (Working Notes). 2193\u20132200."},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.5555\/2390374.2390377"},{"key":"e_1_3_2_57_2","first-page":"88","volume-title":"SRW@ HLT-NAACL","author":"Waseem Zeerak","year":"2016","unstructured":"Zeerak Waseem and Dirk Hovy. 2016. Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter. In SRW@ HLT-NAACL. 88\u201393."},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2806394"},{"key":"e_1_3_2_59_2","unstructured":"Michael Wiegand Melanie Siegel and Josef Ruppenhofer. 2018. Overview of the germeval 2018 shared task on the identification of offensive language. (2018)."},{"key":"e_1_3_2_60_2","unstructured":"YouTube. 2020. Hate speech policy. (2020). Retrieved January 31 2023 from https:\/\/support.google.com\/youtube\/answer\/2801939?hl=en."},{"key":"e_1_3_2_61_2","article-title":"Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval)","author":"Zampieri Marcos","year":"2019","unstructured":"Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019. Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval). arXiv preprint arXiv:1903.08983 (2019).","journal-title":"arXiv preprint arXiv:1903.08983"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_48"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/2833312.2849567"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580476","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580476","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:42Z","timestamp":1750178262000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,17]]},"references-count":62,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,6,30]]}},"alternative-id":["10.1145\/3580476"],"URL":"https:\/\/doi.org\/10.1145\/3580476","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,17]]},"assertion":[{"value":"2022-04-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-01-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}