4 research outputs found
Indian Legal NLP Benchmarks : A Survey
Availability of challenging benchmarks is the key to advancement of AI in a
specific field.Since Legal Text is significantly different than normal English
text, there is a need to create separate Natural Language Processing benchmarks
for Indian Legal Text which are challenging and focus on tasks specific to
Legal Systems. This will spur innovation in applications of Natural language
Processing for Indian Legal Text and will benefit AI community and Legal
fraternity. We review the existing work in this area and propose ideas to
create new benchmarks for Indian Legal Natural Language Processing
Named Entity Recognition in Indian court judgments
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.Comment: to be published in NLLP 2022 Workshop at EMNL
Corpus for Automatic Structuring of Legal Documents
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.Comment: Accepted at LREC 2022, 10 Pages (8 page main paper + 2 page
references
SemEval 2023 Task 6: LegalEval -- Understanding Legal Texts
In populous countries, pending legal cases have been growing exponentially.
There is a need for developing NLP-based techniques for processing and
automatically understanding legal documents. To promote research in the area of
Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at
SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles
Labeling) is about automatically structuring legal documents into semantically
coherent units, Task-B (Legal Named Entity Recognition) deals with identifying
relevant entities in a legal document and Task-C (Court Judgement Prediction
with Explanation) explores the possibility of automatically predicting the
outcome of a legal case along with providing an explanation for the prediction.
In total 26 teams (approx. 100 participants spread across the world) submitted
systems paper. In each of the sub-tasks, the proposed systems outperformed the
baselines; however, there is a lot of scope for improvement. This paper
describes the tasks, and analyzes techniques proposed by various teams.Comment: 13 Pages (9 Pages + References), Accepted at SemEval 202