Classifying sentential modality in legal language: A use case in financial regulations, acts and directives
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O'Neill, James, Buitelaar, Paul, Robin, Cécile, & O'Brien, Leona. (2017). Classifying sentential modality in legal language: a use case in financial regulations, acts and directives. Paper presented at the 16th International Conference on Artificial Intelligence and Law London, London.
Texts expressed in legal language are often di cult and time consuming for lawyers to read through, particularly for the purpose of identifying relevant deontic modalities (obligations, prohibitions and permissions). By nature, the language of law is strict, hence the predominant use of modal logic as a substitute for the syntactical ambiguity in natural language, speci cally, deontic and alethic logic for the respective modalities. However, deontic modalities which express obligations,prohibitions and permissions, can have varying degree and preciseness to which they correspond to a matter, strict deontic logic does not allow for such quantitative measures. Therefore, this paper outlines a data-driven approach by classifying deontic modalities using ensembled Arti cial Neural Networks (ANN) that incorporate domain speci c legal distributional semantic model (DSM) representations, in combination with, a general DSM representation. We propose to use well calibrated probability estimates from these classi ers as an approximation to the degree which an obligation/prohibition or permission belongs to a given class based on SME annotated sentences. Best results show 82.33 % accuracy on a held-out test set.
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