An Intelligent Approach Towards Legal Text-Documents Retrieval

Document retrieval is the process of discovering a set of documents related to a query or another document, as per comparable similarity to some significant extent. Such as legal law, cases, or judgments. The current work under the area of the Document retrieval domain focuses on creating a system that will examine various features or information contained in the supporting documents with the given query document and suggest the possible top-ranked documents which have a higher similarity score. This work is particularly useful in helping out the lawyers by suggesting similar cases or judgments and allowing them to explore significantly less number of previously recorded cases/judgments. The main focus of this work is to retrieve relevant documents for particular Supreme Court cases in India from a set of prior case documents. To categorize documents and do the document retrieval task, we have used a published data set of 2000 prior cases. For each distinct document, the current system lists a set of documents with their ranking scores using N similarity, Fast-text, and BERT. Then, the system returns the relevant documents based on their ranking score. In order to compare our system to other similar ones already in use, we computed the accuracy and recall of our system. We have discovered that various methods perform better in certain areas where a few systems have higher precision and others with higher recall values. Our developed system provides comparably better accuracy, recall, and mean average precision than other current systems.