Automatic detection of arguments in legal texts

Marie-Francine Moens, Erik Boiy, Raquel Mochales Palau, Chris Reed

    Research output: Chapter in Book/Report/Conference proceedingChapter

    129 Citations (Scopus)

    Abstract

    This paper provides the results of experiments on the detection of arguments in texts among which are legal texts. The detection is seen as a classification problem. A classifier is trained on a set of annotated arguments. Different feature sets are evaluated involving lexical, syntactic, semantic and discourse properties of the texts. The experiments are a first step in the context of automatically classifying arguments in legal texts according to their rhetorical type and their visualization for convenient access and search.
    Original languageEnglish
    Title of host publicationProceedings of the 11th International Conference on Artificial Intelligence and Law
    PublisherAssociation for Computing Machinery
    Pages225-230
    Number of pages6
    ISBN (Print)9781595936806
    DOIs
    Publication statusPublished - 2007
    Event11th International Conference on Artificial Intelligence and Law - Palo Alto, United States
    Duration: 4 Jun 20078 Jun 2007
    http://dl.acm.org/citation.cfm?id=1276318&picked=prox

    Conference

    Conference11th International Conference on Artificial Intelligence and Law
    CountryUnited States
    CityPalo Alto
    Period4/06/078/06/07
    Internet address

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  • Cite this

    Moens, M-F., Boiy, E., Palau, R. M., & Reed, C. (2007). Automatic detection of arguments in legal texts. In Proceedings of the 11th International Conference on Artificial Intelligence and Law (pp. 225-230). Association for Computing Machinery. https://doi.org/10.1145/1276318.1276362