Analysing the Resourcefulness of the Paragraph for Precedence Retrieval
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Abstract (arXiv)
Developing methods for extracting relevant legal information to aid legal practitioners is an active research area. In this regard, research efforts are being made by leveraging different kinds of information, such as meta-data, citations, keywords, sentences, paragraphs, etc. Similar to any text document, legal documents are composed of paragraphs. In this paper, we have analyzed the resourcefulness of paragraph-level information in capturing similarity among judgments for improving the performance of precedence retrieval. We found that the paragraph-level methods could capture the similarity among the judgments with only a few paragraph interactions and exhibit more discriminating power over the baseline document-level method. Moreover, the comparison results on two benchmark datasets for the precedence retrieval on the Indian supreme court judgments task show that the paragraph-level methods exhibit comparable performance with the state-of-the-art methods
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References · 5
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
- 2022Legal Case Document Similarity: You Need Both Network and Textcited by 16
- 2020Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case Document Similaritycited by 10
- 2020Methods for Computing Legal Document Similarity: A Comparative Studycited by 9
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