An analysis of topic modelling for legislative texts

dc.contributor.authorO'Neill, James
dc.contributor.authorRobin, Cecile
dc.contributor.authorO'Brien, Leona
dc.contributor.authorBuitelaar, Paul
dc.date.accessioned2018-09-13T11:46:39Z
dc.date.available2018-09-13T11:46:39Z
dc.date.issued2016
dc.description.abstractThe uprise of legislative documents within the past decade has risen dramatically, making it difficult for law practitioners to attend to legislation such as Statutory Instrument orders and Acts. This work focuses on the use of topic models for summarizing and visualizing British legislation, with a view toward easier browsing and identification of salient legal topics and their respective set of topic specific terms. We provide an initial qualitative evaluation from a legal expert on how the models have performed by ranking them for each jurisdiction according to topic coherency and relevance.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO'Neill, J., Robin, C., O'Brien, L. and Buitelaar, P. (2016) 'An analysis of topic modelling for legislative texts', Proceedings of the Second Workshop on Automated Semantic Analysis of Information in Legal Texts, co-located with the 16th International Conference on Artificial Intelligence and Law (ICAIL 2017), London, UK, 16 June.en
dc.identifier.issn1613-0073
dc.identifier.urihttps://hdl.handle.net/10468/6774
dc.language.isoenen
dc.publisherCEUR Workshop Proceedingsen
dc.relation.urihttp://ceur-ws.org/Vol-2143/
dc.rights© 2016, the Authors. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).en
dc.subjectTopic modellingen
dc.subjectDimensionality reduction techniquesen
dc.subjectBayesian inferenceen
dc.subjectTopic coherencyen
dc.titleAn analysis of topic modelling for legislative textsen
dc.typeConference itemen
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