# Named Entity Recognition in Indian court judgments

arXiv: https://arxiv.org/abs/2211.03442 · HTML: https://mlsanity.com/paper/2211.03442/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 19 of 19)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://mlsanity.com/paper/2105.13562/) (2021)
- [Packed Levitated Marker for Entity and Relation Extraction](https://mlsanity.com/paper/2109.06067/) (2021)
- [T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition](https://mlsanity.com/paper/2209.12616/) (2022)
- [Corpus for Automatic Structuring of Legal Documents](https://mlsanity.com/paper/2201.13125/) (2022)
- [A Dataset of German Legal Documents for Named Entity Recognition](https://mlsanity.com/paper/2003.13016/) (2020)
- [Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation](https://mlsanity.com/paper/2210.07544/) (2022)
- [Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition](https://mlsanity.com/paper/2004.14514/) (2020)
- [LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents](https://mlsanity.com/paper/2112.14731/) (2021)
- [Orchestrating NLP Services for the Legal Domain](https://mlsanity.com/paper/2003.12900/) (2020)
- [May I Check Again? -- A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to French Legal Texts](https://mlsanity.com/paper/1909.03453/) (2019)

## Built on by (most cited first, 26 of 26)

- [How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?](https://mlsanity.com/paper/2306.01248/) (2023)
- [Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges](https://mlsanity.com/paper/2410.21306/) (2024)
- [LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text](https://mlsanity.com/paper/2402.04335/) (2024)
- [LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain](https://mlsanity.com/paper/2404.02127/) (2024)
- [Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation](https://mlsanity.com/paper/2310.11049/) (2023)
- [SemEval 2023 Task 6: LegalEval - Understanding Legal Texts](https://mlsanity.com/paper/2304.09548/) (2023)
- [Understand Legal Documents with Contextualized Large Language Models](https://mlsanity.com/paper/2303.12135/) (2023)
- [LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Text](https://mlsanity.com/paper/2410.12064/) (2024)
- [IL-PCSR: Legal Corpus for Prior Case and Statute Retrieval](https://mlsanity.com/paper/2511.00268/) (2025)
- [Aalap: AI Assistant for Legal & Paralegal Functions in India](https://mlsanity.com/paper/2402.01758/) (2024)
- [Large Language Models for Judicial Entity Extraction: A Comparative Study](https://mlsanity.com/paper/2407.05786/) (2024)
- [Citation-Based Summarization of Landmark Judgments](https://mlsanity.com/paper/2406.10824/) (2024)
- [CanLegalRAGBench: Evaluating Retrieval-Augmented Generation on Canadian Case Law](https://mlsanity.com/paper/2605.30497/) (2026)
- [Bonafide at LegalLens 2024 Shared Task: Using Lightweight DeBERTa Based Encoder For Legal Violation Detection and Resolution](https://mlsanity.com/paper/2410.22977/) (2024)
- [Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments](https://mlsanity.com/paper/2506.15266/) (2025)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
- [LexChronos: An Agentic Framework for Structured Event Timeline Extraction in Indian Jurisprudence](https://mlsanity.com/paper/2603.01651/) (2026)
- [Comparative study on Judgment Text Classification for Transformer Based Models](https://mlsanity.com/paper/2306.01739/) (2023)
- [FlairNLP at SemEval-2023 Task 6b: Extraction of Legal Named Entities from Legal Texts using Contextual String Embeddings](https://mlsanity.com/paper/2306.02182/) (2023)
- [A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval](https://mlsanity.com/paper/2507.01058/) (2025)
- [IBPS: Indian Bail Prediction System](https://mlsanity.com/paper/2508.07592/) (2025)
- [AugAbEx: Bridging Abstractive and Extractive Legal Summarization](https://mlsanity.com/paper/2511.12290/) (2025)
- [ILSIC: Corpora for Identifying Indian Legal Statutes from Queries by Laypeople](https://mlsanity.com/paper/2602.00881/) (2026)
- [The GELATO Dataset for Legislative NER](https://mlsanity.com/paper/2603.14130/) (2026)
- [PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation](https://mlsanity.com/paper/2607.09094/) (2026)
- [IntLawNER: A Named Entity Recognition Dataset and Benchmark in International Law](https://mlsanity.com/paper/2609.22529/) (2026)

## Abstract (arXiv)

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.