  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.
