Combining Argument Mining Techniques

John Lawrence; Chris Reed

Combining Argument Mining Techniques

John Lawrence, Chris Reed

Computing

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

55 Citations (Scopus)

Abstract

In this paper, we look at three different methods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, patterns of human seasoning which have been detailed extensively in philosophy and psychology. For each of these approaches we achieve results comparable to those previously reported, whilst at the same time achieving a more detailed argument structure. Finally, we use the results from these individual techniques to apply them in combination, further improving the argument structure identification.

Original language	English
Title of host publication	2nd Workshop on Argumentation Mining
Subtitle of host publication	Proceedings of the Workshop
Place of Publication	Denver
Publisher	Association for Computational Linguistics
Pages	127-136
Number of pages	10
Volume	1
ISBN (Print)	978-1-941643-34-1
Publication status	Published - Jun 2015
Event	2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015): 2nd Workshop on Argumentation Mining 2015 - Sheraton Denver Downtown Hotel, Denver, United States Duration: 31 May 2015 → 5 Jun 2015 http://naacl.org/naacl-hlt-2015/

Conference

Conference	2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015)
Country/Territory	United States
City	Denver
Period	31/05/15 → 5/06/15
Internet address	http://naacl.org/naacl-hlt-2015/

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http://aclweb.org/anthology/W/W15/

Cite this

@inproceedings{621bc25234d54b7690db5e1635beccdd,

title = "Combining Argument Mining Techniques",

abstract = "In this paper, we look at three different methods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, patterns of human seasoning which have been detailed extensively in philosophy and psychology. For each of these approaches we achieve results comparable to those previously reported, whilst at the same time achieving a more detailed argument structure. Finally, we use the results from these individual techniques to apply them in combination, further improving the argument structure identification.",

author = "John Lawrence and Chris Reed",

year = "2015",

month = jun,

language = "English",

isbn = "978-1-941643-34-1",

volume = "1",

pages = "127--136",

booktitle = "2nd Workshop on Argumentation Mining",

publisher = "Association for Computational Linguistics",

note = "2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015) : 2nd Workshop on Argumentation Mining 2015 ; Conference date: 31-05-2015 Through 05-06-2015",

url = "http://naacl.org/naacl-hlt-2015/",

}

Lawrence, J & Reed, C 2015, Combining Argument Mining Techniques. in 2nd Workshop on Argumentation Mining: Proceedings of the Workshop. vol. 1, Association for Computational Linguistics, Denver, pp. 127-136, 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015), Denver, United States, 31/05/15. <http://aclweb.org/anthology/W/W15/>

TY - GEN

T1 - Combining Argument Mining Techniques

AU - Lawrence, John

AU - Reed, Chris

PY - 2015/6

Y1 - 2015/6

N2 - In this paper, we look at three different methods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, patterns of human seasoning which have been detailed extensively in philosophy and psychology. For each of these approaches we achieve results comparable to those previously reported, whilst at the same time achieving a more detailed argument structure. Finally, we use the results from these individual techniques to apply them in combination, further improving the argument structure identification.

AB - In this paper, we look at three different methods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, patterns of human seasoning which have been detailed extensively in philosophy and psychology. For each of these approaches we achieve results comparable to those previously reported, whilst at the same time achieving a more detailed argument structure. Finally, we use the results from these individual techniques to apply them in combination, further improving the argument structure identification.

UR - http://www.cs.cornell.edu/home/cardie/naacl-2nd-arg-mining/index.htm

M3 - Conference contribution

SN - 978-1-941643-34-1

VL - 1

SP - 127

EP - 136

BT - 2nd Workshop on Argumentation Mining

PB - Association for Computational Linguistics

CY - Denver

T2 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015)

Y2 - 31 May 2015 through 5 June 2015

ER -

Combining Argument Mining Techniques

Abstract

Conference

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