Topic Model Based Text Similarity Measure for Chinese Judgment Document

• 作者: Bin Luo Chuanyi Li Jidong Ge Xiaoyu Zhou Yemao Zhou Yi Feng Yue Wang ZhongjinLi
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: CHINESE JUDGMENT documents Data science Machine learning Natural language processing Text similarity TF-IDF TOPIC model LATENT DIRICHLET ALLOCATION Labeled LATENT DIRICHLET ALLOCATION
• 年，卷（期）: 2017,（2）
• 页码范围: 9-11
• 页数: 3页
• 分类号: C5

摘要

In the recent informatization of Chinese courts, the huge amount of law cases and judgment documents, which were digital stored,has provided a good foundation for the research of judicial big data and machine learning. In this situation, some ideas about Chinese courts can reach automation or get better result through the research of machine learning, such as similar documents recommendation, workload evaluation based on similarity of judgement documents and prediction of possible relevant statutes. In trying to achieve all above mentioned, and also in face of the characteristics of Chinese judgement document, we propose a topic model based approach to measure the text similarity of Chinese judgement document, which is based on TF-IDF, Latent Dirichlet Allocation (LDA), Labeled Latent Dirichlet Allocation (LLDA) and other treatments. Combining with the characteristics of Chinese judgment document,we focus on the specific steps of approach, the preprocessing of corpus, the parameters choices of training and the evaluation of similarity measure result. Besides, implementing the approach for prediction of possible statutes and regarding the prediction accuracy as the evaluation metric, we designed experiments to demonstrate the reasonability of decisions in the process of design and the high performance of our approach on text similarity measure. The experiments also show the restriction of our approach which need to be focused in future work.