README.md

metadata

language:
  - en
license: mit
size_categories:
  - 10K<n<100K
task_categories:
  - token-classification
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: annotations
      list:
        - name: result
          list:
            - name: from_name
              dtype: string
            - name: id
              dtype: string
            - name: to_name
              dtype: string
            - name: type
              dtype: string
            - name: value
              struct:
                - name: end
                  dtype: int64
                - name: labels
                  sequence: string
                - name: start
                  dtype: int64
                - name: text
                  dtype: string
    - name: meta
      struct:
        - name: source
          dtype: string
    - name: id
      dtype: string
    - name: data
      struct:
        - name: text
          dtype: string
  splits:
    - name: train
      num_bytes: 7672312
      num_examples: 10995
    - name: dev
      num_bytes: 815588
      num_examples: 1074
    - name: test
      num_bytes: 3376945
      num_examples: 4501
  download_size: 5441938
  dataset_size: 11864845
tags:
  - legal

Dataset for training and evaluating Indian Legal Named Entity Recognition model.

Paper details

Named Entity Recognition in Indian court judgments Arxiv

Label Scheme

View label scheme (14 labels for 1 components)

ENTITY	BELONGS TO
LAWYER	PREAMBLE
COURT	PREAMBLE, JUDGEMENT
JUDGE	PREAMBLE, JUDGEMENT
PETITIONER	PREAMBLE, JUDGEMENT
RESPONDENT	PREAMBLE, JUDGEMENT
CASE_NUMBER	JUDGEMENT
GPE	JUDGEMENT
DATE	JUDGEMENT
ORG	JUDGEMENT
STATUTE	JUDGEMENT
WITNESS	JUDGEMENT
PRECEDENT	JUDGEMENT
PROVISION	JUDGEMENT
OTHER_PERSON	JUDGEMENT

Author - Publication

@inproceedings{kalamkar-etal-2022-named,
    title = "Named Entity Recognition in {I}ndian court judgments",
    author = "Kalamkar, Prathamesh  and
      Agarwal, Astha  and
      Tiwari, Aman  and
      Gupta, Smita  and
      Karn, Saurabh  and
      Raghavan, Vivek",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.nllp-1.15",
    doi = "10.18653/v1/2022.nllp-1.15",
    pages = "184--193",
    abstract = "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.",
}