Legal document clustering with built-in topic segmentation

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2011, Pages 383-392

  Lu, Qiang ^a   Conrad, Jack G ^a   Al Kofahi, Khalid ^a   Keenan, William ^a  

^a THOMSON REUTERS   (United Kingdom)

Author keywords

clustering; topic segmentation; unsupervised learning

Indexed keywords

CLICKSTREAMS; CLUSTERING; CLUSTERING METHODS; DIFFERENTIATORS; DOMAIN EXPERTS; DOMAIN SPECIFIC LANGUAGES; LEGAL DOCUMENTS; LEGAL DOMAINS; LEGAL ISSUES; LEGAL PROFESSION; NEWS SOURCES; RECALL AND PRECISION; TEXTUAL DOCUMENTS; USER BEHAVIORS; VERY LARGE DATABASE;

AUTHENTICATION; BEHAVIORAL RESEARCH; DIGITAL LIBRARIES; INTERNET; KNOWLEDGE MANAGEMENT; LAWS AND LEGISLATION; METADATA; PROBLEM ORIENTED LANGUAGES; QUALITY CONTROL; UNSUPERVISED LEARNING;

CLUSTERING ALGORITHMS;

EID: 83055191922     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2063576.2063636     Document Type: Conference Paper

References (29)

1. K. Al-Kofahi, et al. Combining multiple classifiers for text categorization. In Proceedings of the 10th International Conference on Information and Knowledge Management (CIKM01), pages 97-104, 2001.
2. J. Allen, et al. Topic detection and tracking pilot study - final report. In Proceedings of the DARPA Broadcast News Transcription and understanding Workshop, 1998.
3. D. Beeferman, A. Berger. and J. Lafferty. A model of lexical attraction and repulsion. In Proceedings of the ACL, pages 373-380, 1997.
4. P. Berkhin. A survey of clustering data mining techniques. Grouping Multidimensional Data, pages 26-71, 2005.
5. D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2002.
6. P. Bradley, C. Reina, and U. Fayyad. Clustering very large databases using em mixture models. In Proceedings of ICPR, volume 2, pages 2076-2080, 2000.
7. F. Choi. Advances in domain independent linear text segmentation. In Proceedings of the Association for Computational Linguistics, pages 26-33, 2000.
8. F. Choi, P. Wiemer-Hastings, and J. Moore. Latent semantic analysis for text segmentation. In Proceedings of EMNLP, pages 109-117, 2001.
9. J. Conrad, K. Al-Kofahi, Y. Zhao, and G. Karypis. Effective document clustering for large heterogeneous law firm collections. In Proceedings of the 10th International Conference on Artificial Intelligence and Law (ICAIL05), pages 177-187, 2005.
10. J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters. In Proceedings of the Sixth Symposium on Operating System Design and Implementation (OSDI04), 2004.
11. S. Deerwester, et al. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391-407, 1990.
12. Apache hadoop. http://haoop.apache.org/, 2010.
13. Marti Hearst. Texttiling: Segmenting text into multi-paragraph subtopic passages. Computational Linguistics, 23:33-64, 1997.
14. T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of 22rd Annual International SIGIR Conference, 1999.
15. M.C. Hung and D.L. Yang, An efficient fuzzy c-means clustering algorithm. In Proceedings of the IEEE international Conference on Data Mining, pages 226-232, 2001.
16. R. Kondadadi and R. Kozma. A modified fuzzy art for soft document clustering. In Proc. of International Joint Conference on Neural Networks IJCNN, pages 2545-2549, 2002.
17. H. Kozima, Text segmentation based on similarity between words full text. In Proc. of the ACL, pages 286-288, 1993.
18. H. Kozima and T. Furugori. Similarity between words computed by spreading activation on an english dictionary. In Proceedings of the ACL, pages 232-239, 1993.
19. J. Lin and C. Dyer. Data-intensive text processing with mapreduce. Synthesis Lectures on Human language Technologies, 2010.
20. Apache mahout overview. http://lucene.apache.org.mahout/, 2010.
21. A. McCallum, K. Nigam, and L. Ungat. Efficient clustering of high-dimensional data set with application to reference matching. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD00), pages 189-178, 2000.
22. D. Merkl and E. Schweighofer. En route to data mining in legal text corpora: Clustering, neural computation, and international treaties. In Proceedings of the 8th International Workshop on Database and Expert Systems Applications (DEXA '99), 1997.
23. C. Ordonez and E. Omiecinski. Frem: Fast and robust em clustering for large data sets. In Proceedings of CIKM, pages 590-699, 2002.
24. M. Shafiei and E. Milios. A statistical model for topic segmentation and clustering. Lecture Notes in Computer Science, 5032, 2008.
25. Svm light. http://svmlight.Joachims.org/, 2010.
26. A. Tagarelli and G. Karypis. A segment-based approach to clustering multi-topic documents. In Proceedings of the Test Mining Workshop, SIAM Data Mining Conference, 2008.
27. M. Utiyama and H. Isahara. A statistical model for domain-independent text segmentation. In Proceedings of the ACL, pages 499-506, 2001.
28. N. Vaughn and D. Boley. Automated clustering and extraction of distinctive words in legal documents. Dept. of computer science and engineering report, University of Minnesota, 2001.
29. O. Zamir and O. Etzioni. Web document clustering: A feasibility demonstration. In Proceedings of the 21st Int'l ACM SIGIR Conference on Research end Development in Information Retrieval, pages 46-54, 1998.