Publikation: GRIT : A Dataset of Group Reference Recognition in Italian
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For the analysis of political discourse a reliable identification of group references, i.e., linguistic components that refer to individuals or groups of people, is useful. However, the task of automatically recognizing group references has not yet gained much attention within NLP. To address this gap, we introduce GRIT (Group Reference for Italian), a large-scale, multi-domain manually annotated dataset for group reference recognition in Italian. GRIT represents a new resource for automatic and generalizable recognition of group references. With this dataset, we aim to establish group reference recognition as a valid classification task, which extends the domain of Named Entity Recognition by expanding its focus to literal and figurative mentions of social groups. We verify the potential of achieving automated group reference recognition for Italian through an experiment employing a fine-tuned BERT model. Our experimental results substantiate the validity of the task, implying a huge potential for applying automated systems to multiple fields of analysis, such as political text or social media analysis.
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ZANOTTO, Sergio, Qi YU, Miriam BUTT, Diego FRASSINELLI, 2024. GRIT : A Dataset of Group Reference Recognition in Italian. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Torino, Italia, 20. Mai 2024 - 25. Mai 2024. In: CALZOLARI, Nicoletta, Hrsg., Min-Yen KAN, Hrsg., Veronique HOSTE, Hrsg. und andere. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ACL, 2024, S. 7963-7970BibTex
@inproceedings{Zanotto2024Datas-71000, year={2024}, title={GRIT : A Dataset of Group Reference Recognition in Italian}, url={https://aclanthology.org/2024.lrec-main.701}, publisher={ACL}, booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, pages={7963--7970}, editor={Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique}, author={Zanotto, Sergio and Yu, Qi and Butt, Miriam and Frassinelli, Diego} }
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