Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules

Basit Ali, Sachin Pawar, Girish Palshikar, Rituraj Singh

Abstract
Argumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments. Support and Attack relations are the backbone of any legal argument. However, there is no publicly available dataset of these relations in the context of legal arguments expressed in court judgements. In this paper, we focus on automatically constructing such a dataset of Support and Attack relations between sentences in a court judgment with reasonable accuracy. We propose three sets of rules based on linguistic knowledge and distant supervision to identify such relations from Indian Supreme Court judgments. The first rule set is based on multiple discourse connectors, the second rule set is based on common semantic structures between argumentative sentences in a close neighbourhood, and the third rule set uses the information about the source of the argument. We also explore a BERT-based sentence pair classification model which is trained on this dataset. We release the dataset of 20506 sentence pairs - 10746 Support (precision 77.3%) and 9760 Attack (precision 65.8%). We believe that this dataset and the ideas explored in designing the linguistic rules and will boost the argumentation mining research for legal arguments.
Anthology ID:
2022.lrec-1.51
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
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Pages:
491–500
Language:
URL:
https://aclanthology.org/2022.lrec-1.51
DOI:
Bibkey:
Cite (ACL):
Basit Ali, Sachin Pawar, Girish Palshikar, and Rituraj Singh. 2022. Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 491–500, Marseille, France. European Language Resources Association.
Cite (Informal):
Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules (Ali et al., LREC 2022)
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