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--- |
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tags: |
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- spacy |
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- token-classification |
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widget: |
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- text: >- |
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Section 319 Cr.P.C. contemplates a situation where the evidence adduced by |
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the prosecution for Respondent No.3-G. Sambiah on 20th June 1984 |
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- text: | |
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In The High Court Of Kerala At Ernakulam |
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Crl Mc No. 1622 of 2006() |
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1. T.R.Ajayan, S/O. O.Raman, |
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... Petitioner |
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Vs |
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1. M.Ravindran, |
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... Respondent |
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2. Mrs. Nirmala Dinesh, W/O. Dinesh, |
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For Petitioner :Sri.A.Kumar |
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For Respondent :Smt.M.K.Pushpalatha |
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The Hon'ble Mr. Justice P.R.Raman |
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The Hon'ble Mr. Justice V.K.Mohanan |
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Dated :07/01/2008 |
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O R D E R |
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language: |
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- en |
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license: apache-2.0 |
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model-index: |
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- name: en_legal_ner_trf |
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results: |
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- task: |
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type: token-classification |
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name: Named Entity Recognition |
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metrics: |
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- type: F1-Score |
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value: 91.076 |
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name: Test F1-Score |
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datasets: |
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- opennyaiorg/InLegalNER |
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--- |
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# Paper details |
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[Named Entity Recognition in Indian court judgments](https://aclanthology.org/2022.nllp-1.15/) |
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|
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--- |
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Indian Legal Named Entity Recognition(NER): Identifying relevant named entities in an Indian legal judgement using legal NER trained on [spacy](https://github.com/explosion/spaCy). |
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|
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### Scores |
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|
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| Type | Score | |
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| --- | --- | |
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| **F1-Score** | **91.076** | |
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| `Precision` | 91.979 | |
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| `Recall` | 90.19 | |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_legal_ner_trf` | |
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| **Version** | `3.2.0` | |
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| **spaCy** | `>=3.2.2,<3.3.0` | |
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| **Default Pipeline** | `transformer`, `ner` | |
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| **Components** | `transformer`, `ner` | |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
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| **Sources** | [InLegalNER Train Data](https://storage.googleapis.com/indianlegalbert/OPEN_SOURCED_FILES/NER/NER_TRAIN.zip) [GitHub](https://github.com/Legal-NLP-EkStep/legal_NER)| |
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| **License** | `MIT` | |
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| **Author** | [Aman Tiwari](https://www.linkedin.com/in/amant555/) | |
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|
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## Load Pretrained Model |
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|
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Install the model using pip |
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|
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```sh |
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pip install https://huggingface.co/opennyaiorg/en_legal_ner_trf/resolve/main/en_legal_ner_trf-any-py3-none-any.whl |
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``` |
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|
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Using pretrained NER model |
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|
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```python |
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# Using spacy.load(). |
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import spacy |
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nlp = spacy.load("en_legal_ner_trf") |
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text = "Section 319 Cr.P.C. contemplates a situation where the evidence adduced by the prosecution for Respondent No.3-G. Sambiah on 20th June 1984" |
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doc = nlp(text) |
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|
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# Print indentified entites |
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for ent in doc.ents: |
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print(ent,ent.label_) |
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|
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##OUTPUT |
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#Section 319 PROVISION |
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#Cr.P.C. STATUTE |
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#G. Sambiah RESPONDENT |
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#20th June 1984 DATE |
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``` |
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|
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### Label Scheme |
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<details> |
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|
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<summary>View label scheme (14 labels for 1 components)</summary> |
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| ENTITY | BELONGS TO | |
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| --- | --- | |
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| `LAWYER` | PREAMBLE | |
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| `COURT` | PREAMBLE, JUDGEMENT | |
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| `JUDGE` | PREAMBLE, JUDGEMENT | |
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| `PETITIONER` | PREAMBLE, JUDGEMENT | |
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| `RESPONDENT` | PREAMBLE, JUDGEMENT | |
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| `CASE_NUMBER` | JUDGEMENT | |
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| `GPE` | JUDGEMENT | |
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| `DATE` | JUDGEMENT | |
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| `ORG` | JUDGEMENT | |
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| `STATUTE` | JUDGEMENT | |
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| `WITNESS` | JUDGEMENT | |
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| `PRECEDENT` | JUDGEMENT | |
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| `PROVISION` | JUDGEMENT | |
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| `OTHER_PERSON` | JUDGEMENT | |
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|
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</details> |
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|
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## Author - Publication |
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|
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``` |
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@inproceedings{kalamkar-etal-2022-named, |
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title = "Named Entity Recognition in {I}ndian court judgments", |
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author = "Kalamkar, Prathamesh and |
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Agarwal, Astha and |
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Tiwari, Aman and |
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Gupta, Smita and |
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Karn, Saurabh and |
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Raghavan, Vivek", |
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booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, United Arab Emirates (Hybrid)", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.nllp-1.15", |
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doi = "10.18653/v1/2022.nllp-1.15", |
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pages = "184--193", |
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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.", |
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} |
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``` |
