  This paper describes our submission to the Competition on Legal Information
Extraction/Entailment 2022 (COLIEE-2022) workshop on case law competition for
tasks 1 and 2. Task 1 is a legal case retrieval task, which involves reading a
new case and extracting supporting cases from the provided case law corpus to
support the decision. Task 2 is the legal case entailment task, which involves
the identification of a paragraph from existing cases that entails the decision
in a relevant case. We employed the neural models Sentence-BERT and Sent2Vec
for semantic understanding and the traditional retrieval model BM25 for exact
matching in both tasks. As a result, our team ("nigam") ranked 5th among all
the teams in Tasks 1 and 2. Experimental results indicate that the traditional
retrieval model BM25 still outperforms neural network-based models.
