Legal Document Similarity-Metric Learning Task

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A Legal Document Similarity-Metric Learning Task is a domain-specific document similarity-metric learning task focused on learning a legal document similarity model that can assess the semantic similarity between legal documents.



References

2022

2022

  • (Bi et al., 2022) ⇒ Shuyang Bi, Zaeem Ali, Miao Wang, Tianming Wu, and Guoqing Qi. (2022). “Learning heterogeneous graph embedding for Chinese legal document similarity." In: Knowledge-Based Systems 244: 108860.
    • QUOTE: "...Measuring the similarity between legal documents to find prior documents from a massive collection that are similar to a current document is an essential component in legal assistant..."
    • NOTE: Proposes graph embedding techniques to learn legal document similarity for applications like prior case retrieval.

2020

  • (Bhattacharya et al., 2020a) ⇒ Prajjwal Bhattacharya, Kripabandhu Ghosh, Arpan Pal, and Saptarshi Ghosh. (2020). “Methods for computing legal document similarity: A comparative study." arXiv preprint arXiv:2005.06032.
    • ABSTRACT: Computing similarity between two legal documents is an important and challenging task in the domain of Legal Information Retrieval. Finding similar legal documents has many applications in downstream tasks, including prior-case retrieval, recommendation of legal articles, and so on. Prior works have proposed two broad ways of measuring similarity between legal documents - analyzing the precedent citation network, and measuring similarity based on textual content similarity measures. But there has not been a comprehensive comparison of these existing methods on a common platform. In this paper, we perform the first systematic analysis of the existing methods. In addition, we explore two promising new similarity computation methods - one text-based and the other based on network embeddings, which have not been considered till now.
    • NOTE: Compares different approaches to learn Legal Document Similarity combining textual signals and citation patterns.

2020