The structure of argument: Semantic mapping of US supreme court cases

Fionn Murtagh, Mohsen Farid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We semantically map out the flow of the narrative involved in a United States Supreme Court case. Our objective is both the static analysis of semantics but, more so, the trajectory of argument. This includes consideration of those who are involved, the Justices and the Attorneys. We study therefore the flow of argument. Geometrical (metric, latent semantic) and topological (ultrametric, hierarchical) analyses are used in our analytics.

Original languageEnglish
Title of host publicationStatistical Learning and Data Sciences SLDS 2015
EditorsAlexander Gammerman, Vladimir Vovk, Harris Papadopoulos
PublisherSpringer Verlag
Pages397-405
Number of pages9
ISBN (Print)9783319170909
DOIs
Publication statusPublished - 3 Apr 2015
Externally publishedYes
Event3rd International Symposium on Statistical Learning and Data Sciences - University of London, Egham, United Kingdom
Duration: 20 Apr 201523 Apr 2015
Conference number: 3
http://www.clrc.rhul.ac.uk/slds2015/ (Link to Conference Website)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9047
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Symposium on Statistical Learning and Data Sciences
Abbreviated titleSLDS 2015
CountryUnited Kingdom
CityEgham
Period20/04/1523/04/15
Internet address

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  • Activities

    • 1 Oral presentation

    The Structure of Argument: Semantic Mapping of US Supreme Court Cases

    Fionn Murtagh (Speaker) & Mohsen Farid (Contributor to Paper or Presentation)

    22 Apr 2015

    Activity: Talk or presentation typesOral presentation

    Cite this

    Murtagh, F., & Farid, M. (2015). The structure of argument: Semantic mapping of US supreme court cases. In A. Gammerman, V. Vovk, & H. Papadopoulos (Eds.), Statistical Learning and Data Sciences SLDS 2015 (pp. 397-405). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9047). Springer Verlag. https://doi.org/10.1007/978-3-319-17091-6_34