{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:37:06Z","timestamp":1767919026561,"version":"3.49.0"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"7","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"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":[[2024,7,31]]},"abstract":"<jats:p>\n            Humour is a crucial aspect of human speech, and it is, therefore, imperative to create a system that can offer such detection. While data regarding humour in English speech is plentiful, the same cannot be said for a low-resource language like Hindi. Through this article, we introduce two multimodal datasets for humour detection in the Hindi web series. The dataset was collected from over 500 minutes of conversations amongst the characters of the Hindi web series\n            <jats:italic>Kota-Factory<\/jats:italic>\n            and\n            <jats:italic>Panchayat<\/jats:italic>\n            . Each dialogue is manually annotated as Humour or Non-Humour. Along with presenting a new Hindi language-based Humour detection dataset, we propose an improved framework for detecting humour in Hindi conversations. We start by preprocessing both datasets to obtain uniformity across the dialogues and datasets. The processed dialogues are then passed through the Skip-gram model for generating Hindi word embedding. The generated Hindi word embedding is then passed onto three convolutional neural network (CNN) architectures simultaneously, each having a different filter size for feature extraction. The extracted features are then passed through stacked Long Short-Term Memory (LSTM) layers for further processing and finally classifying the dialogues as Humour or Non-Humour. We conduct intensive experiments on both proposed Hindi datasets and evaluate several standard performance metrics. The performance of our proposed framework was also compared with several baselines and contemporary algorithms for Humour detection. The results demonstrate the effectiveness of our dataset to be used as a standard dataset for Humour detection in the Hindi web series. The proposed model yields an accuracy of 91.79 and 87.32 while an F1 score of 91.64 and 87.04 in percentage for the\n            <jats:italic>Kota-Factory<\/jats:italic>\n            and\n            <jats:italic>Panchayat<\/jats:italic>\n            datasets, respectively.\n          <\/jats:p>","DOI":"10.1145\/3661306","type":"journal-article","created":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T10:01:36Z","timestamp":1714212096000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["HumourHindiNet: Humour detection in Hindi web series using word embedding and convolutional neural network"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4263-7168","authenticated-orcid":false,"given":"Akshi","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Computing, Goldsmiths University of London, London, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5665-1836","authenticated-orcid":false,"given":"Abhishek","family":"Mallik","sequence":"additional","affiliation":[{"name":"Computer Science &amp; Engineering, Delhi Technological University, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8951-5996","authenticated-orcid":false,"given":"Sanjay","family":"Kumar","sequence":"additional","affiliation":[{"name":"Computer Science &amp; Engineering, Delhi Technological University, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"496","volume-title":"Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC\u201916)","author":"Bertero Dario","year":"2016","unstructured":"Dario Bertero and Pascale Fung. 2016. Deep learning of audio and language features for humor prediction. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC\u201916). 496\u2013501."},{"key":"e_1_3_1_3_2","first-page":"130","volume-title":"Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Bertero Dario","year":"2016","unstructured":"Dario Bertero and Pascale Fung. 2016. A long short-term memory framework for predicting humor in dialogues. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 130\u2013135."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3462244.3479959"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.18653\/v1\/W17-5009","volume-title":"Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications","author":"Chen Lei","year":"2017","unstructured":"Lei Chen and Chungmin Lee. 2017. Predicting audience\u2019s laughter during presentations using convolutional neural network. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. 86\u201390."},{"key":"e_1_3_1_6_2","first-page":"113","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)","author":"Chen Peng-Yu","year":"2018","unstructured":"Peng-Yu Chen and Von-Wun Soo. 2018. Humor recognition using deep learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 113\u2013117."},{"key":"e_1_3_1_7_2","first-page":"2019","article-title":"Humor detection in yelp reviews","volume":"15","author":"Oliveira Luke De","year":"2015","unstructured":"Luke De Oliveira and Alfredo L. Rodrigo. 2015. Humor detection in yelp reviews. Retrieved on December 15 (2015), 2019.","journal-title":"Retrieved on December"},{"key":"e_1_3_1_8_2","article-title":"Pre-training of deep bidirectional transformers for language understanding","author":"Devlin J.","year":"2019","unstructured":"J. Devlin, M. W. Chang, K. Lee, and K. B. Toutanova. 2019. Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, MN: Association for Computational Linguistics, 4171\u201386.","journal-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.02.030"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3576913"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Md Kamrul Hasan Wasifur Rahman Amir Zadeh Jianyuan Zhong Md Iftekhar Tanveer and Louis-Philippe Morency. 2019. UR-FUNNY: A multimodal language dataset for understanding humor. arXiv:1904.06618. Retrieved from https:\/\/arxiv.org\/abs\/1904.06618","DOI":"10.18653\/v1\/D19-1211"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1193"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Armand Joulin Edouard Grave Piotr Bojanowski and Tomas Mikolov. 2016. Bag of tricks for efficient text classification. arXiv:1607.01759. Retrieved from https:\/\/arxiv.org\/abs\/1607.01759","DOI":"10.18653\/v1\/E17-2068"},{"key":"e_1_3_1_14_2","article-title":"Analysis of cursive text recognition systems: A systematic literature review","author":"Khan Sulaiman","year":"2023","unstructured":"Sulaiman Khan, Shah Nazir, and Habib Ullah Khan. 2023. Analysis of cursive text recognition systems: A systematic literature review. ACM Transactions on Asian and Low-Resource Language Information Processing 22, 7 (2023), 1\u201330.","journal-title":"ACM Transactions on Asian and Low-Resource Language Information Processing"},{"key":"e_1_3_1_15_2","first-page":"89","volume-title":"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies","author":"Kiddon Chloe","year":"2011","unstructured":"Chloe Kiddon and Yuriy Brun. 2011. That\u2019s what she said: Double entendre identification. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 89\u201394."},{"issue":"1","key":"e_1_3_1_16_2","first-page":"1","article-title":"Sentiment analysis in Hindi\u2013A survey on the state-of-the-art techniques","volume":"21","author":"Kulkarni Dhanashree S.","year":"2021","unstructured":"Dhanashree S. Kulkarni and Sunil S. Rodd. 2021. Sentiment analysis in Hindi\u2013A survey on the state-of-the-art techniques. Transactions on Asian and Low-Resource Language Information Processing 21, 1 (2021), 1\u201346.","journal-title":"Transactions on Asian and Low-Resource Language Information Processing"},{"key":"e_1_3_1_17_2","article-title":"Negative stances detection from multilingual data streams in low-resource languages on social media using BERT and CNN-based transfer learning model","author":"Kumar Sanjay","unstructured":"Sanjay Kumar. 2024. Negative stances detection from multilingual data streams in low-resource languages on social media using BERT and CNN-based transfer learning model. ACM Transactions on Asian and Low-Resource Language Information Processing 23, 1 (2024), 1\u201318.","journal-title":"ACM Transactions on Asian and Low-Resource Language Information Processing"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-12739-w"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Paul Pu Liang Ziyin Liu Amir Zadeh and Louis-Philippe Morency. 2018. Multimodal language analysis with recurrent multistage fusion. arXiv:1808.03920. Retrieved from https:\/\/arxiv.org\/abs\/1808.03920","DOI":"10.18653\/v1\/D18-1014"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1095-6"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2662002"},{"key":"e_1_3_1_22_2","first-page":"531","volume-title":"Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing","author":"Mihalcea Rada","year":"2005","unstructured":"Rada Mihalcea and Carlo Strapparava. 2005. Making computers laugh: Investigations in automatic humor recognition. In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing. 531\u2013538."},{"key":"e_1_3_1_23_2","article-title":"Distributed representations of words and phrases and their compositionality","volume":"26","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems 26 (2013).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_24_2","first-page":"204","volume-title":"Proceedings of the 3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2018) Co-located with 34th Conference of the Spanish Society for Natural Language Processing (SEPLN \u201918)","author":"Ortega-Bueno Reynier","year":"2018","unstructured":"Reynier Ortega-Bueno, Carlos E. Muniz-Cuza, Jos\u00e9 E. Medina Pagola, and Paolo Rosso. 2018. UO UPV: Deep linguistic humor detection in Spanish social media. In Proceedings of the 3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2018) Co-located with 34th Conference of the Spanish Society for Natural Language Processing (SEPLN \u201918). 204\u2013213."},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016892"},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","DOI":"10.4324\/9780203984567","volume-title":"The Language of Humour","author":"Ross Alison","year":"2005","unstructured":"Alison Ross. 2005. The Language of Humour. Routledge."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580476"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017216"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3551876.3554802"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.18653\/v1\/D15-1284","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"Yang Diyi","year":"2015","unstructured":"Diyi Yang, Alon Lavie, Chris Dyer, and Eduard Hovy. 2015. Humor recognition and humor anchor extraction. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2367\u20132376."},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12021"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401077"}],"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\/3661306","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3661306","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:02Z","timestamp":1750291442000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3661306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,26]]},"references-count":31,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7,31]]}},"alternative-id":["10.1145\/3661306"],"URL":"https:\/\/doi.org\/10.1145\/3661306","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,26]]},"assertion":[{"value":"2024-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-10","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}