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

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

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

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

Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

42 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the First Workshop on Argumentation Mining
Subtitle of host publication	ACL 2014
Publisher	Association for Computational Linguistics
Pages	79-87
Number of pages	9
ISBN (Electronic)	978-1-941643-06-8
Publication status	Published - 2014
Event	First Workshop on Argumentation Mining: 52nd Annual Meeting of the Association for Computational Linguistics - Baltimore, United States Duration: 26 Jun 2014 → … http://www.uncg.edu/cmp/ArgMining2014/ http://acl2014.org/home.htm

Conference

Conference	First Workshop on Argumentation Mining
Country/Territory	United States
City	Baltimore
Period	26/06/14 → …
Internet address	http://www.uncg.edu/cmp/ArgMining2014/ http://acl2014.org/home.htm

Access to Document

http://acl2014.org/acl2014/W14-21/index.html

Cite this

@inproceedings{5e42c8162be347049f506a8795813ca0,

title = "Mining Arguments From 19th Century Philosophical Texts Using Topic Based Modelling",

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. ",

author = "John Lawrence and Chris Reed and Simon McAlister and Andrew Ravenscroft and Colin Allen and David Bourget",

year = "2014",

language = "English",

pages = "79--87",

booktitle = "Proceedings of the First Workshop on Argumentation Mining",

publisher = "Association for Computational Linguistics",

note = "First Workshop on Argumentation Mining : 52nd Annual Meeting of the Association for Computational Linguistics ; Conference date: 26-06-2014",

url = "http://www.uncg.edu/cmp/ArgMining2014/, http://acl2014.org/home.htm",

}

Lawrence, J , Reed, C, McAlister, S, Ravenscroft, A, Allen, C & Bourget, D 2014, Mining Arguments From 19th Century Philosophical Texts Using Topic Based Modelling. in Proceedings of the First Workshop on Argumentation Mining : ACL 2014. Association for Computational Linguistics, pp. 79-87, First Workshop on Argumentation Mining, Baltimore, United States, 26/06/14. <http://acl2014.org/acl2014/W14-21/index.html>

TY - GEN

T1 - Mining Arguments From 19th Century Philosophical Texts Using Topic Based Modelling

AU - Lawrence, John

AU - Reed, Chris

AU - McAlister, Simon

AU - Ravenscroft, Andrew

AU - Allen, Colin

AU - Bourget, David

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

M3 - Conference contribution

SP - 79

EP - 87

BT - Proceedings of the First Workshop on Argumentation Mining

PB - Association for Computational Linguistics

T2 - First Workshop on Argumentation Mining

Y2 - 26 June 2014

ER -

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

Abstract

Conference

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Cite this