{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:38:28Z","timestamp":1778575108296,"version":"3.51.4"},"reference-count":68,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T00:00:00Z","timestamp":1745366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key Research and Development Program of Yunnan Province","award":["202203AA080004"],"award-info":[{"award-number":["202203AA080004"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>Word Sense Disambiguation (WSD) in Natural Language Processing (NLP) is crucial for discerning the correct meaning of words with multiple senses in various contexts. Recent advancements in this field, particularly Deep Learning (DL) and sophisticated language models such as BERT and GPT, have significantly improved WSD performance. However, challenges persist, especially with languages like Urdu, which are known for their linguistic complexity and limited digital resources compared to English. This study addresses the challenge of advancing WSD in Urdu by developing and applying tailored Data Augmentation (DA) techniques. We introduce an innovative approach, Prompt Engineering with Retrieval Augmented Generation (RAG), leveraging GPT-3.5-turbo to generate context-sensitive Gloss Definitions (GD). Additionally, we employ sentence-level and word-level DA techniques, including Back Translation (BT) and Masked Word Prediction (MWP). To enhance sentence understanding, we combine three BERT embedding models: mBERT, mDistilBERT, and Roberta_Urdu, facilitating a more nuanced comprehension of sentences and improving word disambiguation in complex linguistic contexts. Furthermore, we propose a novel network architecture merging Transformer Encoder (TE)-CNN and TE-BiLSTM models with Multi-Head Self-Attention (MHSA), One-Dimensional Convolutional Neural Network (1DCNN), and Bidirectional Long Short-Term Memory (BiLSTM). This architecture is tailored to address polysemy and capture short- and long-range dependencies critical for effective WSD in Urdu. Empirical evaluations on Lexical Sample (LS) and All Word (AW) tasks demonstrate the effectiveness of our approach, achieving an 88.9% F1 score on the LS and a 79.2% F1 score on AW tasks. These results underscore the importance of language-specific approaches and the potential of DA and advanced modeling techniques in overcoming challenges associated with WSD in languages with limited resources.<\/jats:p>","DOI":"10.1145\/3719293","type":"journal-article","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T10:16:21Z","timestamp":1740219381000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Urdu Word Sense Disambiguation: Leveraging Contextual Stacked Embedding, Siamese Transformer Encoder 1DCNN-BiLSTM, and Gloss Data Augmentation"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6223-875X","authenticated-orcid":false,"given":"Anil","family":"Ahmed","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8860-7805","authenticated-orcid":false,"given":"Degen","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2121-1865","authenticated-orcid":false,"given":"Syed Yasser","family":"Arafat","sequence":"additional","affiliation":[{"name":"Department of Computer Science and information Technology, Mirpur University of Science and Technology, Mirpur, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5003-7969","authenticated-orcid":false,"given":"Khawaja Iftekhar","family":"Rashid","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s10586-017-0918-0","article-title":"Urdu word sense disambiguation using machine learning approach","volume":"21","author":"Abid Muhammad","year":"2018","unstructured":"Muhammad Abid, Asad Habib, Jawad Ashraf, and Abdul Shahid. 2018. Urdu word sense disambiguation using machine learning approach. Cluster Comput. 21 (2018), 515\u2013522.","journal-title":"Cluster Comput."},{"issue":"2","key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1080\/17517575.2020.1755455","article-title":"Exploring deep learning approaches for Urdu text classification in product manufacturing","volume":"16","author":"Akhter Muhammad Pervez","year":"2022","unstructured":"Muhammad Pervez Akhter, Zheng Jiangbin, Irfan Raza Naqvi, Mohammed Abdelmajeed, and Muhammad Fayyaz. 2022. Exploring deep learning approaches for Urdu text classification in product manufacturing. Enterp. Inf. Syst. 16, 2 (2022), 223\u2013248.","journal-title":"Enterp. Inf. Syst."},{"key":"e_1_3_2_4_2","first-page":"1","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi Laith","year":"2021","unstructured":"Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, Jos\u00e9 Santamar\u00eda, Mohammed A. Fadhel, Muthana Al-Amidie, and Laith Farhan. 2021. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8 (2021), 1\u201374.","journal-title":"J. Big Data"},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"5437","DOI":"10.1007\/s00521-020-05321-8","article-title":"Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification","volume":"33","author":"Asim Muhammad Nabeel","year":"2021","unstructured":"Muhammad Nabeel Asim, Muhammad Usman Ghani, Muhammad Ali Ibrahim, Waqar Mahmood, Andreas Dengel, and Sheraz Ahmed. 2021. Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification. Neural Comput. Applic. 33 (2021), 5437\u20135469.","journal-title":"Neural Comput. Applic."},{"key":"e_1_3_2_6_2","first-page":"4330","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Bevilacqua Michele","year":"2021","unstructured":"Michele Bevilacqua, Tommaso Pasini, Alessandro Raganato, and Roberto Navigli. 2021. Recent trends in word sense disambiguation: A survey. In Proceedings of the International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence, Inc., 4330\u20134338."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_3_2_8_2","first-page":"1877","volume-title":"Advances in Neural Information Processing Systems","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askellet al.2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877\u20131901. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf"},{"key":"e_1_3_2_9_2","first-page":"106","volume-title":"Proceedings of the 2nd International Symposium on Computational and Business Intelligence","author":"Chandra Ganesh","year":"2014","unstructured":"Ganesh Chandra and Snajay K. Dwivedi. 2014. A literature survey on various approaches of word sense disambiguation. In Proceedings of the 2nd International Symposium on Computational and Business Intelligence. IEEE, 106\u2013109."},{"key":"e_1_3_2_10_2","article-title":"Unleashing the potential of prompt engineering in large language models: A comprehensive review","author":"Chen Banghao","year":"2023","unstructured":"Banghao Chen, Zhaofeng Zhang, Nicolas Langren\u00e9, and Shengxin Zhu. 2023. Unleashing the potential of prompt engineering in large language models: A comprehensive review. arXiv preprint arXiv:2310.14735 (2023).","journal-title":"arXiv preprint arXiv:2310.14735"},{"key":"e_1_3_2_11_2","first-page":"144","volume-title":"Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD","author":"Chouikhi Hasna","year":"2021","unstructured":"Hasna Chouikhi, Hamza Chniter, and Fethi Jarray. 2021. Stacking BERT based models for Arabic sentiment analysis. In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD\u201921). 144\u2013150."},{"key":"e_1_3_2_12_2","first-page":"4171","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171\u20134186. DOI:DOI:10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s10115-014-0753-z","article-title":"A comparative study between possibilistic and probabilistic approaches for monolingual word sense disambiguation","volume":"44","author":"Elayeb Bilel","year":"2015","unstructured":"Bilel Elayeb, Ibrahim Bounhas, Oussama Ben Khiroun, Fabrice Evrard, and Narj\u00e8s Bellamine Ben Saoud. 2015. A comparative study between possibilistic and probabilistic approaches for monolingual word sense disambiguation. Knowl. Inf. Syst. 44 (2015), 91\u2013126.","journal-title":"Knowl. Inf. Syst."},{"key":"e_1_3_2_14_2","first-page":"6645","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing","author":"Graves Alex","year":"2013","unstructured":"Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 6645\u20136649."},{"issue":"1","key":"e_1_3_2_15_2","first-page":"215","article-title":"Word sense disambiguation for Arabic text categorization.","volume":"13","author":"Hadni Meryeme","year":"2016","unstructured":"Meryeme Hadni, Sa\u00efd El Alaoui Ouatik, and Abdelmonaime Lachkar. 2016. Word sense disambiguation for Arabic text categorization. Int. Arab J. Inf. Technol. 13, 1A (2016), 215\u2013222.","journal-title":"Int. Arab J. Inf. Technol."},{"key":"e_1_3_2_16_2","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1996. LSTM can solve hard long time lag problems. In Proceedings of the 10th International Conference on Neural Information Processing Systems (NIPS\u201996) MIT Press Denver Colorado 473\u2013479."},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s10462-009-9117-6","article-title":"Performing word sense disambiguation at the border between unsupervised and knowledge-based techniques","volume":"30","author":"Hristea Florentina","year":"2008","unstructured":"Florentina Hristea, Marius Popescu, and Monica Dumitrescu. 2008. Performing word sense disambiguation at the border between unsupervised and knowledge-based techniques. Artif. Intell. Rev. 30 (2008), 67\u201386.","journal-title":"Artif. Intell. Rev."},{"key":"e_1_3_2_18_2","first-page":"116","volume-title":"Proceedings of ACL\u201918, System Demonstrations","author":"Junczys-Dowmunt Marcin","year":"2018","unstructured":"Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychevet al.2018. Marian: Fast neural machine translation in C++. In Proceedings of ACL\u201918, System Demonstrations, Fei Liu and Thamar Solorio (Eds.). Association for Computational Linguistics, 116\u2013121. DOI:DOI:10.18653\/v1\/P18-4020"},{"issue":"2","key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"3341","DOI":"10.3233\/JIFS-211275","article-title":"A deep learning approach to building a framework for Urdu POS and NER","volume":"44","author":"Kazi Samreen","year":"2023","unstructured":"Samreen Kazi, Maria Rahim, and Shakeel Khoja. 2023. A deep learning approach to building a framework for Urdu POS and NER. J. Intell. Fuzzy Syst. 44, 2 (2023), 3341\u20133351.","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"1","key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"5436","DOI":"10.1038\/s41598-022-09381-9","article-title":"Multi-class sentiment analysis of Urdu text using multilingual BERT","volume":"12","author":"Khan Lal","year":"2022","unstructured":"Lal Khan, Ammar Amjad, Noman Ashraf, and Hsien-Tsung Chang. 2022. Multi-class sentiment analysis of Urdu text using multilingual BERT. Scient. Rep. 12, 1 (2022), 5436.","journal-title":"Scient. Rep."},{"key":"e_1_3_2_21_2","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR) San Diego California 6."},{"key":"e_1_3_2_22_2","first-page":"303","volume-title":"Proceedings of the European Conference on Information Retrieval (ECIR\u201921)","author":"Kohli Harsh","year":"2021","unstructured":"Harsh Kohli. 2021. Transfer learning and augmentation for word sense disambiguation. In Proceedings of the European Conference on Information Retrieval (ECIR\u201921). Springer, 303\u2013311."},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.18653\/v1\/2020.lifelongnlp-1.3","volume-title":"Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems","author":"Kumar Varun","year":"2020","unstructured":"Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020. Data augmentation using pre-trained transformer models. In Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems, William M. Campbell, Alex Waibel, Dilek Hakkani-Tur, Timothy J. Hazen, Kevin Kilgour, Eunah Cho, Varun Kumar, and Hadrien Glaude (Eds.). Association for Computational Linguistics, 18\u201326. Retrieved from https:\/\/aclanthology.org\/2020.lifelongnlp-1.3"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.18653\/v1\/2023.acl-long.88","volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Kwon Sunjae","year":"2023","unstructured":"Sunjae Kwon, Rishabh Garodia, Minhwa Lee, Zhichao Yang, and Hong Yu. 2023. Vision meets definitions: Unsupervised visual word sense disambiguation incorporating gloss information. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (Eds.). Association for Computational Linguistics, 1583\u20131598. DOI:DOI:10.18653\/v1\/2023.acl-long.88"},{"key":"e_1_3_2_25_2","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive NLP tasks","volume":"33","author":"Lewis Patrick","year":"2020","unstructured":"Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K\u00fcttler, Mike Lewis, Wen-tau Yih, Tim Rockt\u00e4schel et\u00a0al. 2020. Retrieval-augmented generation for knowledge-intensive NLP tasks. Advan. Neural Inf. Process. Syst. 33 (2020), 9459\u20139474.","journal-title":"Advan. Neural Inf. Process. Syst."},{"key":"e_1_3_2_26_2","unstructured":"Guan-Ting Lin and M. Dina Giambi. 2021. Context-gloss augmentation for improving word sense disambiguation. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:238856837"},{"key":"e_1_3_2_27_2","unstructured":"Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy Mike Lewis Luke Zettlemoyer and Veselin Stoyanov. 2020. RoBERTa: A robustly optimizedBERT pretraining approach. Retrieved from https:\/\/openreview.net\/forum?id=SyxS0T4tvS"},{"issue":"2","key":"e_1_3_2_28_2","first-page":"387","article-title":"Analysis and evaluation of language models for word sense disambiguation","volume":"47","author":"Loureiro Daniel","year":"2021","unstructured":"Daniel Loureiro, Kiamehr Rezaee, Mohammad Taher Pilehvar, and Jose Camacho-Collados. 2021. Analysis and evaluation of language models for word sense disambiguation. Computat. Ling. 47, 2 (2021), 387\u2013443.","journal-title":"Computat. Ling."},{"issue":"1","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.gltp.2022.04.020","article-title":"A review: Data pre-processing and data augmentation techniques","volume":"3","author":"Maharana Kiran","year":"2022","unstructured":"Kiran Maharana, Surajit Mondal, and Bhushankumar Nemade. 2022. A review: Data pre-processing and data augmentation techniques. Global Transit. Proc. 3, 1 (2022), 91\u201399.","journal-title":"Global Transit. Proc."},{"key":"e_1_3_2_30_2","first-page":"254","volume-title":"Proceedings of the 12th Global Wordnet Conference","author":"Malaysha Sanad","year":"2023","unstructured":"Sanad Malaysha, Mustafa Jarrar, and Mohammed Khalilia. 2023. Context-gloss augmentation for improving Arabic target sense verification. In Proceedings of the 12th Global Wordnet Conference, German Rigau, Francis Bond, and Alexandre Rademaker (Eds.). Global Wordnet Association, 254\u2013262."},{"key":"e_1_3_2_31_2","first-page":"5542","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Malkin Nikolay","year":"2021","unstructured":"Nikolay Malkin, Sameera Lanka, Pranav Goel, Sudha Rao, and Nebojsa Jojic. 2021. GPT perdetry test: Generating new meanings for new words. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5542\u20135553."},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/978-981-15-3383-9_54","article-title":"Deep learning techniques: An overview","author":"Mathew Amitha","year":"2021","unstructured":"Amitha Mathew, P. Amudha, and S. Sivakumari. 2021. Deep learning techniques: An overview. In Proceedings of the Conference on Advanced Machine Learning Technologies and Applications (AMLTA\u201920). 599\u2013608.","journal-title":"Proceedings of the Conference on Advanced Machine Learning Technologies and Applications (AMLTA\u201920)"},{"key":"e_1_3_2_33_2","volume-title":"Proceedings of the 1st International Conference on Learning Representations (ICLR\u201913)","author":"Mikolov Tom\u00e1s","year":"2013","unstructured":"Tom\u00e1s Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Proceedings of the 1st International Conference on Learning Representations (ICLR\u201913). Retrieved from http:\/\/arxiv.org\/abs\/1301.3781"},{"issue":"2","key":"e_1_3_2_34_2","first-page":"1","article-title":"Recent advances in natural language processing via large pre-trained language models: A survey","volume":"56","author":"Min Bonan","year":"2023","unstructured":"Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2023. Recent advances in natural language processing via large pre-trained language models: A survey. Comput. Surv. 56, 2 (2023), 1\u201340.","journal-title":"Comput. Surv."},{"issue":"3","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.csl.2004.05.007","article-title":"Word sense disambiguation of WordNet glosses","volume":"18","author":"Moldovan Dan","year":"2004","unstructured":"Dan Moldovan and Adrian Novischi. 2004. Word sense disambiguation of WordNet glosses. Comput. Speech Lang. 18, 3 (2004), 301\u2013317.","journal-title":"Comput. Speech Lang."},{"key":"e_1_3_2_36_2","article-title":"Supervised word sense disambiguation for Urdu using Bayesian classification","author":"Naseer Asma","year":"2009","unstructured":"Asma Naseer and Sarmad Hussain. 2009. Supervised word sense disambiguation for Urdu using Bayesian classification. Center for Research in Urdu Language Processing, Lahore, Pakistan (2009). https:\/\/www.cle.org.pk\/clt10\/papers\/Supervised%20Word%20Sense%20Disambiguation%20for%20Urdu%20Using%20Bayesian.pdf","journal-title":"Center for Research in Urdu Language Processing, Lahore, Pakistan"},{"issue":"2","key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1459352.1459355","article-title":"Word sense disambiguation: A survey","volume":"41","author":"Navigli Roberto","year":"2009","unstructured":"Roberto Navigli. 2009. Word sense disambiguation: A survey. ACM Comput. Surv. 41, 2 (2009), 1\u201369.","journal-title":"ACM Comput. Surv."},{"issue":"7","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1109\/TPAMI.2005.149","article-title":"Structural semantic interconnections: A knowledge-based approach to word sense disambiguation","volume":"27","author":"Navigli Roberto","year":"2005","unstructured":"Roberto Navigli and Paola Velardi. 2005. Structural semantic interconnections: A knowledge-based approach to word sense disambiguation. IEEE Trans. Pattern Anal. Mach. Intell. 27, 7 (2005), 1075\u20131086.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_3_2_39_2","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.18653\/v1\/2023.semeval-1.281","volume-title":"Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval\u201923)","author":"Ogezi Michael","year":"2023","unstructured":"Michael Ogezi, Bradley Hauer, Talgat Omarov, Ning Shi, and Grzegorz Kondrak. 2023. UAlberta at SemEval-2023 Task 1: Context augmentation and translation for multilingual visual word sense disambiguation. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval\u201923), Atul Kr. Ojha, A. Seza Do\u011fru\u00f6z, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, and Elisa Sartori (Eds.). Association for Computational Linguistics, 2043\u20132051. DOI:DOI:10.18653\/v1\/2023.semeval-1.281"},{"key":"e_1_3_2_40_2","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang Long","year":"2022","unstructured":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray et\u00a0al. 2022. Training language models to follow instructions with human feedback. Advan. Neural Inf. Process. Syst. 35 (2022), 27730\u201327744.","journal-title":"Advan. Neural Inf. Process. Syst."},{"key":"e_1_3_2_41_2","first-page":"1532","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP\u201914)","author":"Pennington Jeffrey","year":"2014","unstructured":"Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP\u201914), Alessandro Moschitti, Bo Pang, and Walter Daelemans (Eds.). Association for Computational Linguistics, 1532\u20131543. DOI:DOI:10.3115\/v1\/D14-1162"},{"issue":"5","key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1093\/jamia\/ocy189","article-title":"deepBioWSD: Effective deep neural word sense disambiguation of biomedical text data","volume":"26","author":"Pesaranghader Ahmad","year":"2019","unstructured":"Ahmad Pesaranghader, Stan Matwin, Marina Sokolova, and Ali Pesaranghader. 2019. deepBioWSD: Effective deep neural word sense disambiguation of biomedical text data. J. Am. Med. Inform. Assoc. 26, 5 (2019), 438\u2013446.","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"e_1_3_2_43_2","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1 8 (2019) 9."},{"issue":"140","key":"e_1_3_2_44_2","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140 (2020), 1\u201367.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s40747-019-0115-2","article-title":"Improvement of query-based text summarization using word sense disambiguation","volume":"6","author":"Rahman Nazreena","year":"2020","unstructured":"Nazreena Rahman and Bhogeswar Borah. 2020. Improvement of query-based text summarization using word sense disambiguation. Complex Intell. Syst. 6 (2020), 75\u201385.","journal-title":"Complex Intell. Syst."},{"issue":"9","key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"6643","DOI":"10.1016\/j.jksuci.2021.07.022","article-title":"An unsupervised method for word sense disambiguation","volume":"34","author":"Rahman Nazreena","year":"2022","unstructured":"Nazreena Rahman and Bhogeswar Borah. 2022. An unsupervised method for word sense disambiguation. J. King Saud Univ.-Comput. Inf. Sci. 34, 9 (2022), 6643\u20136651.","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"e_1_3_2_47_2","doi-asserted-by":"crossref","first-page":"105051","DOI":"10.1016\/j.bspc.2023.105051","article-title":"Morphology & word sense disambiguation embedded multimodal neural machine translation system between Sanskrit and Malayalam","volume":"85","author":"Rahul C.","year":"2023","unstructured":"C. Rahul, T. Arathi, Lakshmi S. Panicker, and R. Gopikakumari. 2023. Morphology & word sense disambiguation embedded multimodal neural machine translation system between Sanskrit and Malayalam. Biomed. Signal Process. Contr. 85 (2023), 105051.","journal-title":"Biomed. Signal Process. Contr."},{"key":"e_1_3_2_48_2","first-page":"650","volume-title":"Proceedings of the Machine Learning for Healthcare Conference","author":"Ranjit Mercy","year":"2023","unstructured":"Mercy Ranjit, Gopinath Ganapathy, Ranjit Manuel, and Tanuja Ganu. 2023. Retrieval augmented chest x-ray report generation using OpenAI GPT models. In Proceedings of the Machine Learning for Healthcare Conference. PMLR, 650\u2013666."},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing Association for Computational Linguistics. Retrieved from http:\/\/arxiv.org\/abs\/1908.10084","DOI":"10.18653\/v1\/D19-1410"},{"issue":"2","key":"e_1_3_2_50_2","first-page":"1","article-title":"Investigating the feasibility of deep learning methods for Urdu word sense disambiguation","volume":"21","author":"Saeed Ali","year":"2021","unstructured":"Ali Saeed, Rao Muhammad Adeel Nawab, and Mark Stevenson. 2021. Investigating the feasibility of deep learning methods for Urdu word sense disambiguation. Trans. Asian Low-resour. Lang. Inf. Process. 21, 2 (2021), 1\u201316.","journal-title":"Trans. Asian Low-resour. Lang. Inf. Process."},{"issue":"4","key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3314940","article-title":"A sense annotated corpus for all-words Urdu word sense disambiguation","volume":"18","author":"Saeed Ali","year":"2019","unstructured":"Ali Saeed, Rao Muhammad Adeel Nawab, Mark Stevenson, and Paul Rayson. 2019. A sense annotated corpus for all-words Urdu word sense disambiguation. ACM Trans. Asian Low-resour. Lang. Inf. Process. 18, 4 (2019), 1\u201314.","journal-title":"ACM Trans. Asian Low-resour. Lang. Inf. Process."},{"key":"e_1_3_2_52_2","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s10579-018-9438-7","article-title":"A word sense disambiguation corpus for Urdu","volume":"53","author":"Saeed Ali","year":"2019","unstructured":"Ali Saeed, Rao Muhammad Adeel Nawab, Mark Stevenson, and Paul Rayson. 2019. A word sense disambiguation corpus for Urdu. Lang. Resour. Eval. 53 (2019), 397\u2013418.","journal-title":"Lang. Resour. Eval."},{"issue":"11","key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3623379","article-title":"Stacking of BERT and CNN models for Arabic word sense disambiguation","volume":"22","author":"Saidi Rakia","year":"2023","unstructured":"Rakia Saidi and Fethi Jarray. 2023. Stacking of BERT and CNN models for Arabic word sense disambiguation. ACM Trans. Asian Low-resour. Lang. Inf. Process. 22, 11 (2023), 1\u201314.","journal-title":"ACM Trans. Asian Low-resour. Lang. Inf. Process."},{"key":"e_1_3_2_54_2","first-page":"203","volume-title":"Proceedings of the International Conference on Computational Collective Intelligence","author":"Saidi Rakia","year":"2023","unstructured":"Rakia Saidi, Fethi Jarray, Asma Akacha, and Wissem Aribi. 2023. WSDTN a novel dataset for Arabic word sense disambiguation. In Proceedings of the International Conference on Computational Collective Intelligence. Springer, 203\u2013212."},{"key":"e_1_3_2_55_2","volume-title":"Proceedings of the Pacific Asia Conference on Language, Information and Computation","author":"Saidi Rakia","year":"2022","unstructured":"Rakia Saidi, Fethi Jarray, Jeongwoo Kang, and Didier Schwab. 2022. GPT-2 contextual data augmentation for word sense disambiguation. In Proceedings of the Pacific Asia Conference on Language, Information and Computation."},{"key":"e_1_3_2_56_2","volume-title":"Proceedings of the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing @ NeurIPS\u201919)","author":"Sanh Victor","year":"2019","unstructured":"Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. In Proceedings of the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing @ NeurIPS\u201919). Retrieved from http:\/\/arxiv.org\/abs\/1910.01108"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","unstructured":"Bianca Scarlini Tommaso Pasini and Roberto Navigli. 2020. With more contexts comes better performance: Contextualized sense embeddings for all-round word sense disambiguation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online 3528\u20133539. DOI:10.18653\/v1\/2020.emnlp-main.285","DOI":"10.18653\/v1\/2020.emnlp-main.285"},{"key":"e_1_3_2_58_2","first-page":"130","volume-title":"Proceedings of the 17th International Conference on Artificial Intelligence: Methodology, Systems, and Applications (AIMSA\u201916)","author":"Simov Kiril","year":"2016","unstructured":"Kiril Simov, Petya Osenova, and Alexander Popov. 2016. Using context information for knowledge-based word sense disambiguation. In Proceedings of the 17th International Conference on Artificial Intelligence: Methodology, Systems, and Applications (AIMSA\u201916). Springer, 130\u2013139."},{"issue":"8","key":"e_1_3_2_59_2","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1007\/s00521-019-04581-3","article-title":"Word sense disambiguation for Punjabi language using deep learning techniques","volume":"32","author":"Singh Varinder Pal","year":"2020","unstructured":"Varinder Pal Singh and Parteek Kumar. 2020. Word sense disambiguation for Punjabi language using deep learning techniques. Neural Comput. Applic. 32, 8 (2020), 2963\u20132973.","journal-title":"Neural Comput. Applic."},{"key":"e_1_3_2_60_2","first-page":"2089","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"Song Xinying","year":"2021","unstructured":"Xinying Song, Alex Salcianu, Yang Song, Dave Dopson, and Denny Zhou. 2021. Fast WordPiece tokenization. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 2089\u20132103. DOI:DOI:10.18653\/v1\/2021.emnlp-main.160"},{"key":"e_1_3_2_61_2","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1145\/860435.860466","volume-title":"Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval","author":"Stokoe Christopher","year":"2003","unstructured":"Christopher Stokoe, Michael P. Oakes, and John Tait. 2003. Word sense disambiguation in information retrieval revisited. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval. 159\u2013166."},{"key":"e_1_3_2_62_2","unstructured":"NLLB Team Marta R. Costa-Juss\u00e0 James Cross Onur \u00c7elebi Maha Elbayad Kenneth Heafield Kevin Heffernan Elahe Kalbassi Janice Lam Daniel Lichtet al.2022. No language left behind: Scaling human-centered machine translation. Retrieved from https:\/\/arxiv.org\/abs\/2207.04672"},{"key":"e_1_3_2_63_2","unstructured":"S. M. Towhidul Islam Tonmoy S. M. Mehedi Zaman Vinija Jain Anku Rani Vipula Rawte Aman Chadha and Amitava Das. 2024. A comprehensive survey of hallucination mitigation techniques in large language models. Retrieved from https:\/\/arxiv.org\/abs\/2401.01313"},{"key":"e_1_3_2_64_2","volume-title":"Advances in Neural Information. Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information. Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"e_1_3_2_65_2","first-page":"5218","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Wang Ming","year":"2021","unstructured":"Ming Wang and Yinglin Wang. 2021. Word sense disambiguation: Towards interactive context exploitation from both word and sense perspectives. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 5218\u20135229."},{"key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"105030","DOI":"10.1016\/j.knosys.2019.105030","article-title":"Word sense disambiguation: A comprehensive knowledge exploitation framework","volume":"190","author":"Wang Yinglin","year":"2020","unstructured":"Yinglin Wang, Ming Wang, and Hamido Fujita. 2020. Word sense disambiguation: A comprehensive knowledge exploitation framework. Knowl.-based Syst. 190 (2020), 105030.","journal-title":"Knowl.-based Syst."},{"key":"e_1_3_2_67_2","first-page":"38","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations","author":"Wolf Thomas","year":"2020","unstructured":"Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowiczet al.2020. Transformers: State-of-the-art natural language processing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Qun Liu and David Schlangen (Eds.). Association for Computational Linguistics, 38\u201345. DOI:DOI:10.18653\/v1\/2020.emnlp-demos.6"},{"key":"e_1_3_2_68_2","unstructured":"Biao Zhang and Rico Sennrich. 2019. Root mean square layer normalization. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc. Red Hook NY USA."},{"key":"e_1_3_2_69_2","doi-asserted-by":"crossref","first-page":"14202","DOI":"10.1109\/ACCESS.2023.3243574","article-title":"Multi-head self-attention gated-dilated convolutional neural network for word sense disambiguation","volume":"11","author":"Zhang Chun-Xiang","year":"2023","unstructured":"Chun-Xiang Zhang, Yu-Long Zhang, and Xue-Yao Gao. 2023. Multi-head self-attention gated-dilated convolutional neural network for word sense disambiguation. IEEE Access 11 (2023), 14202\u201314210.","journal-title":"IEEE Access"}],"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\/3719293","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3719293","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:43:21Z","timestamp":1750272201000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3719293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,23]]},"references-count":68,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5,31]]}},"alternative-id":["10.1145\/3719293"],"URL":"https:\/\/doi.org\/10.1145\/3719293","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,23]]},"assertion":[{"value":"2024-03-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}