@inproceedings{antici-etal-2024-corpus,
title = "A Corpus for Sentence-Level Subjectivity Detection on {E}nglish News Articles",
author = "Antici, Francesco and
Ruggeri, Federico and
Galassi, Andrea and
Korre, Katerina and
Muti, Arianna and
Bardi, Alessandra and
Fedotova, Alice and
Barr{\'o}n-Cede{\~n}o, Alberto",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.25/",
pages = "273--285",
abstract = "We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. We evaluate state-of-the-art multilingual transformer-based models on the task in mono-, multi-, and cross-language settings. For this purpose, we re-annotate an existing Italian corpus. We observe that models trained in the multilingual setting achieve the best performance on the task."
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%0 Conference Proceedings
%T A Corpus for Sentence-Level Subjectivity Detection on English News Articles
%A Antici, Francesco
%A Ruggeri, Federico
%A Galassi, Andrea
%A Korre, Katerina
%A Muti, Arianna
%A Bardi, Alessandra
%A Fedotova, Alice
%A Barrón-Cedeño, Alberto
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F antici-etal-2024-corpus
%X We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. We evaluate state-of-the-art multilingual transformer-based models on the task in mono-, multi-, and cross-language settings. For this purpose, we re-annotate an existing Italian corpus. We observe that models trained in the multilingual setting achieve the best performance on the task.
%U https://aclanthology.org/2024.lrec-main.25/
%P 273-285
Markdown (Informal)
[A Corpus for Sentence-Level Subjectivity Detection on English News Articles](https://aclanthology.org/2024.lrec-main.25/) (Antici et al., LREC-COLING 2024)
ACL
- Francesco Antici, Federico Ruggeri, Andrea Galassi, Katerina Korre, Arianna Muti, Alessandra Bardi, Alice Fedotova, and Alberto Barrón-Cedeño. 2024. A Corpus for Sentence-Level Subjectivity Detection on English News Articles. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 273–285, Torino, Italia. ELRA and ICCL.