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.

Keywords:
Computer science Natural language processing Identification (biology) Artificial intelligence Named-entity recognition Legal document Legal case Baseline (sea) Block (permutation group theory) Named entity Information retrieval Law Political science Engineering

Metrics

32
Cited By
29.43
FWCI (Field Weighted Citation Impact)
33
Refs
1.00
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Artificial Intelligence in Law
Social Sciences →  Social Sciences →  Political Science and International Relations
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence

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