  Document retrieval has taken its role in almost all domains of knowledge
understanding, including the legal domain. Precedent refers to a court decision
that is considered as authority for deciding subsequent cases involving
identical or similar facts or similar legal issues. In this work, we propose
different unsupervised approaches to solve the task of identifying relevant
precedents to a given query case. Our proposed approaches are using word
embeddings like word2vec, doc2vec, and sent2vec, finding cosine similarity
using TF-IDF, retrieving relevant documents using BM25 scores, using the
pre-trained model and SBERT to find the most similar document, and using the
product of BM25 and TF-IDF scores to find the most relevant document for a
given query. We compared all the methods based on precision@10, recall@10, and
MRR. Based on the comparative analysis, we found that the TF-IDF score
multiplied by the BM25 score gives the best result. In this paper, we have also
presented the analysis that we did to improve the BM25 score.
