dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Lichouri, Mohamed Lounnas, Khaled Ouaras, Khelil Rafik Abi, Mohamed Guechtouli, Anis |
| author_facet | Lichouri, Mohamed Lounnas, Khaled Ouaras, Khelil Rafik Abi, Mohamed Guechtouli, Anis |
| contents | This study compares Term Frequency-Inverse Document Frequency (TF-IDF) features with Sentence Transformers for detecting writers' stances--favorable, opposing, or neutral--towards three significant topics: COVID-19 vaccine, digital transformation, and women empowerment. Through empirical evaluation, we demonstrate that Sentence Transformers outperform TF-IDF features across various experimental setups. Our team, dzStance, participated in a stance detection competition, achieving the 13th position (74.91%) among 15 teams in Women Empowerment, 10th (73.43%) in COVID Vaccine, and 12th (66.97%) in Digital Transformation. Overall, our team's performance ranked 13th (71.77%) among all participants. Notably, our approach achieved promising F1-scores, highlighting its effectiveness in identifying writers' stances on diverse topics. These results underscore the potential of Sentence Transformers to enhance stance detection models for addressing critical societal issues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13603 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers Lichouri, Mohamed Lounnas, Khaled Ouaras, Khelil Rafik Abi, Mohamed Guechtouli, Anis Computation and Language This study compares Term Frequency-Inverse Document Frequency (TF-IDF) features with Sentence Transformers for detecting writers' stances--favorable, opposing, or neutral--towards three significant topics: COVID-19 vaccine, digital transformation, and women empowerment. Through empirical evaluation, we demonstrate that Sentence Transformers outperform TF-IDF features across various experimental setups. Our team, dzStance, participated in a stance detection competition, achieving the 13th position (74.91%) among 15 teams in Women Empowerment, 10th (73.43%) in COVID Vaccine, and 12th (66.97%) in Digital Transformation. Overall, our team's performance ranked 13th (71.77%) among all participants. Notably, our approach achieved promising F1-scores, highlighting its effectiveness in identifying writers' stances on diverse topics. These results underscore the potential of Sentence Transformers to enhance stance detection models for addressing critical societal issues. |
| title | dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.13603 |