@inproceedings{meghdadi-inkpen-2024-uottawa,
    title = "u{O}ttawa at {L}egal{L}ens-2024: Transformer-based Classification Experiments",
    author = "Meghdadi, Nima  and
      Inkpen, Diana",
    editor = "Aletras, Nikolaos  and
      Chalkidis, Ilias  and
      Barrett, Leslie  and
      Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina  and
      Preoțiuc-Pietro, Daniel  and
      Spanakis, Gerasimos",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024",
    month = nov,
    year = "2024",
    address = "Miami, FL, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.nllp-1.4/",
    doi = "10.18653/v1/2024.nllp-1.4",
    pages = "42--47",
    abstract = "This paper presents the methods used for LegalLens-2024, 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."
}