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dc.contributor.authorO'Neill, James
dc.contributor.authorBuitelaar, Paul
dc.contributor.authorRobin, Cécile
dc.contributor.authorO'Brien, Leona
dc.date.accessioned2017-09-29T11:59:09Z
dc.date.available2017-09-29T11:59:09Z
dc.date.issued2017-06-12
dc.identifier.citationO'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.en_IE
dc.identifier.isbn978-1-4503-3522-XYZ
dc.identifier.urihttp://hdl.handle.net/10379/6845
dc.description.abstractTexts 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.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherACMen_IE
dc.relation.ispartofInternation Conference on Artificial Intelligence and Lawen
dc.subjectData analyticsen_IE
dc.subjectLegal languageen_IE
dc.subjectFinancial regulationsen_IE
dc.subjectFinancial actsen_IE
dc.subjectFinancial directivesen_IE
dc.subjectLanguageen_IE
dc.subjectLawen_IE
dc.subjectSentence classifcationen_IE
dc.subjectDeontic modalityen_IE
dc.subjectFinancial lawen_IE
dc.titleClassifying sentential modality in legal language: A use case in financial regulations, acts and directivesen_IE
dc.typeConference Paperen_IE
dc.date.updated2017-09-28T14:30:22Z
dc.identifier.doi10.475/123_4
dc.local.publishedsourcehttps://dl.acm.org/proceedings.cfmen_IE
dc.description.peer-reviewednon-peer-reviewed
dc.contributor.funder|~|
dc.internal.rssid13205955
dc.local.contactCécile Robin, -. - Email: cecile.c.robin@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionPUBLISHED
nui.item.downloads531


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