Nullpointer at CheckThat! 2024: Identifying Subjectivity from Multilingual Text Sequence
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914870241263616 |
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| author | Biswas, Md. Rafiul Abir, Abrar Tasneem Zaghouani, Wajdi |
| author_facet | Biswas, Md. Rafiul Abir, Abrar Tasneem Zaghouani, Wajdi |
| contents | This study addresses a binary classification task to determine whether a text sequence, either a sentence or paragraph, is subjective or objective. The task spans five languages: Arabic, Bulgarian, English, German, and Italian, along with a multilingual category. Our approach involved several key techniques. Initially, we preprocessed the data through parts of speech (POS) tagging, identification of question marks, and application of attention masks. We fine-tuned the sentiment-based Transformer model 'MarieAngeA13/Sentiment-Analysis-BERT' on our dataset. Given the imbalance with more objective data, we implemented a custom classifier that assigned greater weight to objective data. Additionally, we translated non-English data into English to maintain consistency across the dataset. Our model achieved notable results, scoring top marks for the multilingual dataset (Macro F1=0.7121) and German (Macro F1=0.7908). It ranked second for Arabic (Macro F1=0.4908) and Bulgarian (Macro F1=0.7169), third for Italian (Macro F1=0.7430), and ninth for English (Macro F1=0.6893). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10252 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nullpointer at CheckThat! 2024: Identifying Subjectivity from Multilingual Text Sequence Biswas, Md. Rafiul Abir, Abrar Tasneem Zaghouani, Wajdi Computation and Language This study addresses a binary classification task to determine whether a text sequence, either a sentence or paragraph, is subjective or objective. The task spans five languages: Arabic, Bulgarian, English, German, and Italian, along with a multilingual category. Our approach involved several key techniques. Initially, we preprocessed the data through parts of speech (POS) tagging, identification of question marks, and application of attention masks. We fine-tuned the sentiment-based Transformer model 'MarieAngeA13/Sentiment-Analysis-BERT' on our dataset. Given the imbalance with more objective data, we implemented a custom classifier that assigned greater weight to objective data. Additionally, we translated non-English data into English to maintain consistency across the dataset. Our model achieved notable results, scoring top marks for the multilingual dataset (Macro F1=0.7121) and German (Macro F1=0.7908). It ranked second for Arabic (Macro F1=0.4908) and Bulgarian (Macro F1=0.7169), third for Italian (Macro F1=0.7430), and ninth for English (Macro F1=0.6893). |
| title | Nullpointer at CheckThat! 2024: Identifying Subjectivity from Multilingual Text Sequence |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.10252 |