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        <title>uOttawa at LegalLens-2024: Transformer-based Classification Experiments</title>
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    <name type="personal">
        <namePart type="given">Nima</namePart>
        <namePart type="family">Meghdadi</namePart>
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        <namePart type="given">Diana</namePart>
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            <title>Proceedings of the Natural Legal Language Processing Workshop 2024</title>
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        <name type="personal">
            <namePart type="given">Nikolaos</namePart>
            <namePart type="family">Aletras</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ilias</namePart>
            <namePart type="family">Chalkidis</namePart>
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        <name type="personal">
            <namePart type="given">Leslie</namePart>
            <namePart type="family">Barrett</namePart>
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            <namePart type="given">Cătălina</namePart>
            <namePart type="family">Goan\textcommabelowtă</namePart>
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            <namePart type="given">Daniel</namePart>
            <namePart type="family">Preo\textcommabelowtiuc-Pietro</namePart>
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        <name type="personal">
            <namePart type="given">Gerasimos</namePart>
            <namePart type="family">Spanakis</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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                <placeTerm type="text">Miami, FL, USA</placeTerm>
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    <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.</abstract>
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%0 Conference Proceedings
%T uOttawa at LegalLens-2024: Transformer-based Classification Experiments
%A Meghdadi, Nima
%A Inkpen, Diana
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F meghdadi-inkpen-2024-uottawa
%X 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.
%U https://aclanthology.org/2024.nllp-1.4
%P 42-47

Markdown (Informal)

[uOttawa at LegalLens-2024: Transformer-based Classification Experiments](https://aclanthology.org/2024.nllp-1.4) (Meghdadi & Inkpen, NLLP 2024)

• uOttawa at LegalLens-2024: Transformer-based Classification Experiments (Meghdadi & Inkpen, NLLP 2024)

ACL

• Nima Meghdadi and Diana Inkpen. 2024. uOttawa at LegalLens-2024: Transformer-based Classification Experiments. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 42–47, Miami, FL, USA. Association for Computational Linguistics.