Smita Gupta


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Named Entity Recognition in Indian court judgments
Prathamesh Kalamkar | Astha Agarwal | Aman Tiwari | Smita Gupta | Saurabh Karn | Vivek Raghavan
Proceedings of the Natural Legal Language Processing Workshop 2022

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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Corpus for Automatic Structuring of Legal Documents
Prathamesh Kalamkar | Aman Tiwari | Astha Agarwal | Saurabh Karn | Smita Gupta | Vivek Raghavan | Ashutosh Modi
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment documents in English that are segmented into topical and coherent parts. Each of these parts is annotated with a label coming from a list of pre-defined Rhetorical Roles. We develop baseline models for automatically predicting rhetorical roles in a legal document based on the annotated corpus. Further, we show the application of rhetorical roles to improve performance on the tasks of summarization and legal judgment prediction. We release the corpus and baseline model code along with the paper.