uOttawa at LegalLens-2024: Transformer-based Classification Experiments
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929569183825920 |
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| author | Meghdadi, Nima Inkpen, Diana |
| author_facet | Meghdadi, Nima Inkpen, Diana |
| contents | This paper presents the methods used for LegalLens-2024 shared task, which focused on detecting legal violations within unstructured textual data and associating these violations with potentially affected individuals. The shared task included two subtasks: A) Legal Named Entity Recognition (L-NER) and B) Legal Natural Language Inference (L-NLI). For subtask A, we utilized the spaCy library, while for subtask B, we employed a combined model incorporating RoBERTa and CNN. Our results were 86.3% in the L-NER subtask and 88.25% in the L-NLI subtask. Overall, our paper demonstrates the effectiveness of transformer models in addressing complex tasks in the legal domain. The source code for our implementation is publicly available at https://github.com/NimaMeghdadi/uOttawa-at-LegalLens-2024-Transformer-based-Classification |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21139 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | uOttawa at LegalLens-2024: Transformer-based Classification Experiments Meghdadi, Nima Inkpen, Diana Computation and Language This paper presents the methods used for LegalLens-2024 shared task, which focused on detecting legal violations within unstructured textual data and associating these violations with potentially affected individuals. The shared task included two subtasks: A) Legal Named Entity Recognition (L-NER) and B) Legal Natural Language Inference (L-NLI). For subtask A, we utilized the spaCy library, while for subtask B, we employed a combined model incorporating RoBERTa and CNN. Our results were 86.3% in the L-NER subtask and 88.25% in the L-NLI subtask. Overall, our paper demonstrates the effectiveness of transformer models in addressing complex tasks in the legal domain. The source code for our implementation is publicly available at https://github.com/NimaMeghdadi/uOttawa-at-LegalLens-2024-Transformer-based-Classification |
| title | uOttawa at LegalLens-2024: Transformer-based Classification Experiments |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.21139 |