Automatic detection of arguments in legal texts

Marie-Francine Moens; Erik Boiy; Raquel Mochales Palau; Chris Reed

doi:10.1145/1276318.1276362

Automatic detection of arguments in legal texts

Marie-Francine Moens
, Erik Boiy
, Raquel Mochales Palau
, Chris Reed

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

252 Citations (Scopus)

Abstract

This paper provides the results of experiments on the detection of arguments in texts among which are legal texts. The detection is seen as a classification problem. A classifier is trained on a set of annotated arguments. Different feature sets are evaluated involving lexical, syntactic, semantic and discourse properties of the texts. The experiments are a first step in the context of automatically classifying arguments in legal texts according to their rhetorical type and their visualization for convenient access and search.

Original language	English
Title of host publication	Proceedings of the 11th International Conference on Artificial Intelligence and Law
Publisher	Association for Computing Machinery
Pages	225-230
Number of pages	6
ISBN (Print)	9781595936806
DOIs	https://doi.org/10.1145/1276318.1276362
Publication status	Published - 2007
Event	11th International Conference on Artificial Intelligence and Law - Palo Alto, United States Duration: 4 Jun 2007 → 8 Jun 2007 http://dl.acm.org/citation.cfm?id=1276318&picked=prox

Conference

Conference	11th International Conference on Artificial Intelligence and Law
Country/Territory	United States
City	Palo Alto
Period	4/06/07 → 8/06/07
Internet address	http://dl.acm.org/citation.cfm?id=1276318&picked=prox

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10.1145/1276318.1276362

Cite this

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title = "Automatic detection of arguments in legal texts",

abstract = "This paper provides the results of experiments on the detection of arguments in texts among which are legal texts. The detection is seen as a classification problem. A classifier is trained on a set of annotated arguments. Different feature sets are evaluated involving lexical, syntactic, semantic and discourse properties of the texts. The experiments are a first step in the context of automatically classifying arguments in legal texts according to their rhetorical type and their visualization for convenient access and search.",

author = "Marie-Francine Moens and Erik Boiy and Palau, \{Raquel Mochales\} and Chris Reed",

year = "2007",

doi = "10.1145/1276318.1276362",

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booktitle = "Proceedings of the 11th International Conference on Artificial Intelligence and Law",

publisher = "Association for Computing Machinery",

note = "11th International Conference on Artificial Intelligence and Law ; Conference date: 04-06-2007 Through 08-06-2007",

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Moens, M-F, Boiy, E, Palau, RM & Reed, C 2007, Automatic detection of arguments in legal texts. in Proceedings of the 11th International Conference on Artificial Intelligence and Law. Association for Computing Machinery, pp. 225-230, 11th International Conference on Artificial Intelligence and Law, Palo Alto, United States, 4/06/07. https://doi.org/10.1145/1276318.1276362

TY - CHAP

T1 - Automatic detection of arguments in legal texts

AU - Moens, Marie-Francine

AU - Boiy, Erik

AU - Palau, Raquel Mochales

AU - Reed, Chris

PY - 2007

Y1 - 2007

N2 - This paper provides the results of experiments on the detection of arguments in texts among which are legal texts. The detection is seen as a classification problem. A classifier is trained on a set of annotated arguments. Different feature sets are evaluated involving lexical, syntactic, semantic and discourse properties of the texts. The experiments are a first step in the context of automatically classifying arguments in legal texts according to their rhetorical type and their visualization for convenient access and search.

AB - This paper provides the results of experiments on the detection of arguments in texts among which are legal texts. The detection is seen as a classification problem. A classifier is trained on a set of annotated arguments. Different feature sets are evaluated involving lexical, syntactic, semantic and discourse properties of the texts. The experiments are a first step in the context of automatically classifying arguments in legal texts according to their rhetorical type and their visualization for convenient access and search.

UR - https://www.scopus.com/pages/publications/34548009846

U2 - 10.1145/1276318.1276362

DO - 10.1145/1276318.1276362

M3 - Chapter

AN - SCOPUS:34548009846

SN - 9781595936806

SP - 225

EP - 230

BT - Proceedings of the 11th International Conference on Artificial Intelligence and Law

PB - Association for Computing Machinery

T2 - 11th International Conference on Artificial Intelligence and Law

Y2 - 4 June 2007 through 8 June 2007

ER -

Automatic detection of arguments in legal texts

Abstract

Conference

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