Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Self-organizing maps
Recent Studies in Automatic Text Analysis and Document Retrieval
Journal of the ACM (JACM)
Applying genetic algorithms to query optimization in document retrieval
Information Processing and Management: an International Journal
Legal Knowledge Representation: Automatic Text Analysis in Public International and European Law
Legal Knowledge Representation: Automatic Text Analysis in Public International and European Law
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Web page feature selection and classification using neural networks
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Effective document clustering for large heterogeneous law firm collections
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
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Text mining has become an effective tool for analyzing text documents in automated ways. Conceptually, clustering, classification and searching of legal documents to identify patterns in law corpora are of key interest since it aids law experts and police officers in their analyses. In this paper, we develop a document classification, clustering and search methodology based on neural network technology that helps law enforcement department to manage criminal written judgments more efficiently. In order to maintain a manageable number of independent Chinese keywords, we use term extraction scheme to select top-n keywords with the highest frequency as inputs of the Back-Propagation Network (BPN), and select seven criminal categories as target outputs of it. Related legal documents are automatically trained and tested by pre-trained neural network models. In addition, we use Self- Organizing Map (SOM) method to cluster criminal written judgments. The research shows that automatic classification and clustering modules classify and cluster legal documents with a very high accuracy. Finally, the search module which uses the previous results helps users find relevant written judgments of criminal cases.