Applying an Information Retrieval Approach to Retrieve Relevant Articles in the Legal Domain

Ambedkar Kanapala (), Sukomal Pal (), Suresh Dara () and Srikanth Jannu ()
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Ambedkar Kanapala: Geethanjali College of Engineering and Technology
Sukomal Pal: Indian Institute of Technology (BHU)
Suresh Dara: Woxsen University
Srikanth Jannu: Vaagdevi Engineering College

Annals of Data Science, 2024, vol. 11, issue 5, No 4, 1563-1580

Abstract: Abstract Retrieving legal texts is an important step for building a question answering system on law domain, which needs relevant articles to answer a query. Remarkable research has been done on legal information retrieval. However, retrieving relevant articles for a question is an extremely challenging task. In this paper, we describe a novel approach to retrieve relevant civil law article for a question from legal bar exams. We used three models Hiemstra, BM25 and PL2F implemented within Terrier. Our system retrieves top-ranked document from the collection according to the models specified and it outputs one single document per query. The best model has been selected on the basis of voting algorithm. Appropriate civil law articles are then retrieved using a mapping between document pair-id and the articles. The system achieved an accuracy of over 71.16% of correct civil law articles on training data and moderate scores on test data.

Keywords: Information retrieval; Legal text; Law articles; Hiemstra; BM25; PL2F; Terrier (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-022-00442-4

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