@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",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preo{\textcommabelow{t}}iuc-Pietro, Daniel",
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.",
}
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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.</abstract>
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%0 Conference Proceedings
%T Named Entity Recognition in Indian court judgments
%A Kalamkar, Prathamesh
%A Agarwal, Astha
%A Tiwari, Aman
%A Gupta, Smita
%A Karn, Saurabh
%A Raghavan, Vivek
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%S Proceedings of the Natural Legal Language Processing Workshop 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F kalamkar-etal-2022-named
%X 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.
%R 10.18653/v1/2022.nllp-1.15
%U https://aclanthology.org/2022.nllp-1.15
%U https://doi.org/10.18653/v1/2022.nllp-1.15
%P 184-193
Markdown (Informal)
[Named Entity Recognition in Indian court judgments](https://aclanthology.org/2022.nllp-1.15) (Kalamkar et al., NLLP 2022)
ACL
- Prathamesh Kalamkar, Astha Agarwal, Aman Tiwari, Smita Gupta, Saurabh Karn, and Vivek Raghavan. 2022. Named Entity Recognition in Indian court judgments. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 184–193, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.