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Mining Arguments From 19th Century Philosophical Texts Using Topic Based Modelling

Mining Arguments From 19th Century Philosophical Texts Using Topic Based Modelling

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

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Authors

  • John Lawrence
  • Chris Reed
  • Simon McAlister
  • Andrew Ravenscroft
  • Colin Allen
  • David Bourget

Research units

Info

Original languageEnglish
Title of host publicationProceedings of the First Workshop on Argumentation Mining
Subtitle of host publicationACL 2014
PublisherAssociation for Computational Linguistics
Pages79-87
Number of pages9
ISBN (Electronic)978-1-941643-06-8
StatePublished - 2014
EventFirst Workshop on Argumentation Mining - Baltimore, United States

Conference

ConferenceFirst Workshop on Argumentation Mining
CountryUnited States
CityBaltimore
Period26/06/14 → …
Internet address

Abstract

In this paper we look at the manual analysis of arguments and how this compares
to the current state of automatic argument analysis. These considerations are used to develop a new approach combining a machine learning algorithm to extract propositions from text, with a topic model to determine argument structure. The results of this method are compared to a manual analysis.

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