Constraint-based Clustering Approach for Retrieving of Relevant Decided Civil Cases in Thai

Civil cases usually refer to legal cases involving private disputes between persons or organizations. After judgment, the civil cases are termed as "decided cases" and the documents may be used for subsequent legal decisions. The substantial number of cases pleaded to the court has caused information overload in the legal area and become a topic of discussion in knowledge management. Some kind of filtering is required to reduce complexity and ease the workload. One possible solution is to group relevant decided cases together. Therefore, our study proposed the automatic retrieval of similar decided civil cases conducted in the Thai language as one cluster. To do this, we utilized a method of constraint-based clustering. Two clustering namely k-means and spherical k-means are compared. Also, three weighting schemes as tf-idf and BM25 were compared. The performance of the proposed method was tested by recall, precision, and F1. Results were satisfactory and acceptable, while the spherical k-means clustering with BM25 term weighting improved the performance of the others. We then selected the best model generated from our method and compared the results to the Multinomial Naïve Bayes and Support Vector Machines classification methods. Our proposed method returned better results than the classification methods, with improved average scores of recall, precision and F1 at 5.33%, 5.48% and 5.41%, respectively.