Predicting the Success of Mediation Requests Based on Heterogeneous Case Properties and Textual Information

Abstract

The main purpose of the mediation committee is to effectively resolve the disputes between people so that the burden of the court can be relieved However a mediation process sometimes consumes much time and human effort The worst result of a mediation is failure If such a case happens people still need to seek formal legal assistance On the other hand the success of mediation is affected by many factors such as the context of the quarrel personality of both parties and the negotiation skill of the mediator which leads to uncertainty thus it can hardly be predicted directly This paper takes a different approach from the previous legal prediction research It analyzes and predicts whether a dispute case in the mediation committee will be resolved in the near future which means two parties reach an agreement peacefully through the conciliation of the mediator The benefits of predicting the success of mediation are two-fold First the parties in a case can consider whether to seek for mediation based on the inference result which can avoid wasting time if the model has a negative prediction Second such inference can further help us to select the leading mediator who is most likely to successfully resolve the dispute Existing works about legal case prediction mostly focused on prosecution or criminal cases In this work we combine the case information and textual features extracted from mediation applications to predict the result of mediation In addition for textual features we apply different state-of-art text mining models and show the comparisons Our experiments show that combining such two kinds of information can effectively achieve 84% for F-measure In addition we further extend the inference model to implement an intelligent function that can recommend suitable mediators based on users’ mediation requests Such function is quite useful for committees to select a right mediator for different cases Our experiments verified our recommender system could judge successfully 95% of mediation cases in testing data We implemented a system for joint functions of predicting the success of mediation request and recommending mediators for Tainan City Government to help mediate disputes

Date of Award	2019
Original language	English
Supervisor	Hsun-Ping Hsieh (Supervisor)