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Named Entity Recognition in Indian court judgments

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arxiv 2211.03442 v1 pith:A2KIRGBA submitted 2022-11-07 cs.CL cs.AI

Prathamesh Kalamkar , Astha Agarwal , Aman Tiwari , Smita Gupta , Saurabh Karn , Vivek Raghavan This is my paper
classification cs.CLcs.AI
keywords legalnamedentitiesentitytextsannotatedapplicationsartificial
verification ladder T0 review T1 audit T2 compute T3 formal
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Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.

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  1. A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval

    cs.IR 2025-06 reject novelty 4.0 of 10

    Fine-tuning Pegasus on LLM-annotated headnotes improves part of the legal summarization pipeline, and a RAG framework retrieves similar Calcutta High Court cases, though retrieval quality is never measured.

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