Precedent Case Retrieval using Wordnet and Deep Recurrent Neural Networks

摘要

The slowness of legal proceedings in the common law legal system is a widely known fact. Anytool which could help reduce the time taken for the resolution of a case is invaluable. Commonlegal systems place a great importance on precedents and retrieving the correct set ofprecedents is considerably time consuming. Hence, for any case whose proceedings are inprogress, if there are suitable prior cases, then the court has to follow the same interpretationsthat were passed in the prior cases. This is to ensure that similar situations receive similartreatment, thus maintaining uniformity amongst the legal proceedings across all courts at alltimes. Hence, precedent cases are treated as important as any other written law (a statute) inthis legal system. In this paper, we propose two new approaches to solve this informationretrieval problem wherein the system accepts the current case document as the query andreturns the relevant precedent cases as the result. The first approach is to calculate thedocument similarity using Wordnet, which is a lexical database that could be leveraged toquantify the semantic relatedness between two documents, using a semantic network. Thesecond approach is the use of a Siamese Manhattan Long Short Term Memory network, whichis a supervised model trained to understand the underlying similarity between two documents.