BERT_LF: A Similar Case Retrieval Method Based on Legal Facts

Abstract

With the development of smart justice in China, the Supreme People's Court implements the system of compulsory retrieval for similar cases continuously and comprehensively, more and more judicial big data has been continuously disclosed, and the application of case retrieval is more extensive, and then, the accuracy of similar case search results needs to be urgently improved. Legal case retrieval is a special search task; for a given query case, it refers to the searching for similar cases. Different from traditional text search, legal case retrieval has different characteristics and greater challenges, for its query case is longer and more complex than common keyword queries and short article queries. In addition, the definition of dependencies between query cases and candidate cases differs from general dependencies based on text or topic. In order to solve these problems, we propose a method for similarity case retrieval based on the legal facts, and our model combine the topic distribution and legal entity facts to make the document representation vector more suitable for legal scenarios. At the same time, the method of paragraph aggregation based on BERT is used to encode context semantic information and solve the problem of long text. The experimental results show that our method is superior to the existing method.