Neural Network Towards Uncertain Legal Case Retrieval

Abstract

Information retrieval in natural language processing is nothing but the process of finding information that matches or satisfies information needed from a large database. Legal Case Retrieval (LCR) is considered to be an uncertain task focused to retrieve a similar case given a query case. Unlike other IR tasks, LCR is more thought-provoking since the query case is much longer and multifaceted when compared to queries in other tasks. Other than that, meaning of similarity between the query case and candidate cases is beyond the topical relevance and hence difficult to build a large-scale case retrieval dataset. Hence, this paper proposes a neural network-based retrieval model that identifies the most similar case supporting the query case decision. To achieve a high recall value, the retrieval process is pipelined into two stages, (i) the candidate cases are initially identified with matching section codes from the query case. (ii) Capture semantic relationship at the point level and then calculate the similarity between query case and candidate case by aggregating point level similarity. With the extensive experiments conducted on the benchmark LCR, the proposed model outperforms the existing work with the value of F-measure 0.82. The initial step of choosing the candidate cases with the matching section code yields a higher recall value and in turn higher F-measure.