{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:40:06Z","timestamp":1772120406138,"version":"3.50.1"},"reference-count":67,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,1,18]],"date-time":"2025-01-18T00:00:00Z","timestamp":1737158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>\n            Social media plays an important role in expressing the thoughts and sentiments of users. Irony is a way of stating a sentiment about something by expressing the opposite of the intended literal meaning. Irony detection is a recent emerging task in low-resource languages, although other tasks related to sentiment, such as sentiment analysis and emotion detection, have been widely tackled. In this study, we investigate Graph Neural Networks (GNNs) for irony detection in Turkish, a low-resource language in sentiment-related tasks. We incorporate semantic information into the GNNs using the Universal Conceptual Cognitive Annotation (UCCA) framework. Extensive experimental results and in-depth analysis show that our models outperform state-of-the-art irony detection models in Turkish. Our UCCA-GAT (UCCA-Graph Attention Network) model achieves an F\n            <jats:sub>1<\/jats:sub>\n            -score of 94.85% (7.362% gain over the state-of-the-art) on the Turkish-Irony-Dataset and an accuracy of 72.82% (4.39% gain over the state-of-the-art) on the IronyTR Dataset. We also provide a comprehensive analysis of the proposed models to understand their limitations.\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n          <\/jats:p>","DOI":"10.1145\/3705610","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T10:53:32Z","timestamp":1732532012000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Semantically-Informed Graph Neural Networks for Irony Detection in Turkish"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8121-3048","authenticated-orcid":false,"given":"Necva","family":"B\u00f6l\u00fcc\u00fc","sequence":"first","affiliation":[{"name":"data61, CSIRO, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1700-0395","authenticated-orcid":false,"given":"Burcu","family":"Can","sequence":"additional","affiliation":[{"name":"Computer Science and Mathematics, University of Stirling, Stirling, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,1,18]]},"reference":[{"key":"e_1_3_7_2_2","first-page":"1","volume-title":"Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013)\u2013Long Papers","author":"Abend Omri","year":"2013","unstructured":"Omri Abend and Ari Rappoport. 2013. UCCA: A semantics-based grammatical annotation scheme. In Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013)\u2013Long Papers. 1\u201312."},{"key":"e_1_3_7_3_2","article-title":"UCCA\u2019s foundational layer: Annotation guidelines v2.1","volume":"2012","author":"Abend Omri","year":"2020","unstructured":"Omri Abend, Nathan Schneider, Dotan Dvir, Jakob Prange, and Ari Rappoport. 2020. UCCA\u2019s foundational layer: Annotation guidelines v2.1. CoRR abs\/2012.15810 (2020). arXiv:2012.15810https:\/\/arxiv.org\/abs\/2012.15810","journal-title":"CoRR"},{"key":"e_1_3_7_4_2","doi-asserted-by":"crossref","first-page":"581","DOI":"10.18653\/v1\/S18-1095","volume-title":"Proceedings of the 12th International Workshop on Semantic Evaluation","author":"Ahmed Usman","year":"2018","unstructured":"Usman Ahmed, Lubna Zafar, Faiza Qayyum, and Muhammad Arshad Islam. 2018. Irony detector at SemEval-2018 task 3: Irony detection in English tweets using word graph. 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