Automated Text Processing Related Short Bibliography


Information Retrieval

[1] Douglas Cutting, David Karger, Jan Pedersen, and John Tukey. Scatter/gather: A cluster-based approach to browsing large document collections. In Proc. ACM SIGIR, pages 318-329, 1992.
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[2] David D. Lewis. Representation and Learning in Information Retrieval. PhD thesis, Department of Computer Science, University of Massachusetts, 1992.
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[3] C. J. van Rijsbergen. Information Retrieval. Butterworths, 2nd edition, 1980.
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[4] Gerard Salton. Automatic Text Processing - The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, 1989.
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Support Vector Machine

[1] Chris Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167, 1998.
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[2] Ronan Collobert and Samy Bengio. Support vector machines for large-scale regression problems. Machine Learning Research, 1:143-160, 2001.
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[3] Nello Cristianini and John Shawe-Taylor. An Introduction to Support Vector Machines and other kernel-base learning methods. Cambridge University Press, 2000.
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[4] K. K. Chin. Support vector machines applied to speech pattern classification. Master's thesis, Engineering Department, Cambridge University, 1999.
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[5] Robert Cooley. Classification of news stories using support vector machines. In Proc. 16th International Joint Conference on Artificial Intelligence Text Mining Workshop, 1999.
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[6] Harris Drucker, Donghui Wu, and Vladimir Vapnik. Support vector machines for Spam categorization. IEEE-NN, 10(5):1048-1054, 1999.
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[7] Susan Dumais and Hao Chen. Hierarchical classification of web content. In Proc. ACM SIGIR, pages 256 - 263, 2000.
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[8] Marti A. Hearst. Trends controversies: Support vector machines. IEEE Intelligent System, 13(4):18-28, 1998.
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[9] Thorsten Joachims. Text categorization with support vector machines: learning with many relevant features. In Proc. 10th European Conference on Machine Learning ECML-98, pages 137-142, 1998.
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[10] Thorsten Joachims. Making large-scale svm learning practical. In Bernhard Schölkopf, Chris Burges, and Alex Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169-185. MIT Press, 1998.
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[11] Thorsten Joachims. Transductive inference for text classification using support vector machines. In Proc. 16th International Conference on Machine Learning, pages 200-209. Morgan Kaufmann, San Francisco, CA, 1999.
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[12] Thorsten Joachims. Estimating the generalization performance of an SVM efficiently. In Proc. 17th International Conf. on Machine Learning, pages 431-438. Morgan Kaufmann, San Francisco, CA, 2000.
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[13] Thorsten Joachims. Transducive inference for text classification using support vector machines. In Proc. International Conference on Machine Learning ICML'99, 1999.
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[14] Bernhard Schölkopf, Chris Burges, and Alex Smola. Introduction to support vector learning. In Bernhard Schölkopf, Chris Burges, and Alex Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 1-22. MIT Press, 1998.
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[15] Bernhard Schölkopf. Support Vector Learning. PhD thesis, Technischen Universität Berlin, 1997. Published by: R. Oldenbourg Verlag, Munich.
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Text Categorization

[1] Chidanand Apte, Fred Damerau, and Sholom M. Weiss. Towards language independent automated learning of text categorisation models. In Research and Development in Information Retrieval, pages 23-30, 1994.
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[2] Chidanand Apte, Fred Damerau, and Sholom M. Weiss. Automated learning of decision rules for text categorization. Information Systems, 12(3):233-251, 1994.
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[3] L. Brewlow and D. Aha. Simplifying decision trees: a survey. Knowledge Engineering Review, 12(1):1-40, 1997.
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[4] Hsinchun Chen. Machine learning for information retrieval: Neural networks, symbolic learning, and genetic algorithms. Journal of the American Society of Information Science, 46(3):194-216, 1995.
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[5] Susan T. Dumais, John Platt, David Hecherman, and Mehran Sahami. Inductive learning algorithms and representations for text categorization. In Proc. 7th International Conference on Information and Knowledge Management CIKM, pages 148-155, 1998.
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[6] Wai Lam and Chao Y. Ho. Using a generalized instance set for automatic text categorization. In Proc. ACM SIGIR-98, pages 81-89, 1998.
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[7] Langley and Stephanie Sage. Oblivious decision trees and abstract cases. In Proc. AAAI-94 Workshop on case-based reasoning, pages 113-117, 1994.
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[8] David D. Lewis and Marc Ringuette. A comparison of two learning algorithms for text categorization. In Proc. Symposium on Document Analysis and Information Retrieval SDAIR-94, pages 81-93, 1994.
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[9] David Lewis. Naive bayes at forty: The independence assumption in information retrieval. In Proc. 10th European Conference on Machine Learning ECML-98, pages 4-15, 1998.
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[10] Andrew McCallum and K. Nigam. A comparison of event models for naive bayes text classification. In Proc. AAAI-98 Workshop on Learning for Text Categorization, 1998.
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[11] Mehran Sahami, Marti Hearst, and Eric Saund. Applying the multiple cause mixture model to text categorization. In Proc. 13th International Conference on Machine Learning ICML96, pages 435-443, 1996.
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[12] Yiming Yang and Jan O. Pedersen. A comparative study on feature selection in text categorization. In Proc. 14th International Conference on Machine Learning, pages 412-420, 1997.
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[13] Yiming Yang and Xin Liu. A re-examination of text categorization methods. In Proc. ACM SIGIR, pages 42-49, Berkley, 1999.
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Event Detection and Tracking

[1] James Allan, Jaime Carbonell, George Doddington, Jonathan Yamron, and Yiming Yang. Topic detection and tracking pilot study final report. In Proc. DARPA Broadcast News Transcription and Understanding Workshop, February 1998.
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[2] James Allan, Victor Lavrenko, and H Jin. First story detection in tdt in hard. In Proc. 9th Conference on Information Knowledge Management CIKM, pages 374-381, McClean, VA USA, 2000.
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[3] James Allan, Ron Papka, and Victor Lavrenko. On-line new event detection and tracking. In Proc. ACM SIGIR, pages 37-45, 1998.
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[4] James Alla, Victor Lavrenko, and Ron Papka. Event tracking. Technical Report CIIR Technical Report IR - 128, Department of Computer Science, University of Massachusetts, 1998.
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[5] Joe Carthy and Alan Smeaton. The design of a topic tracking system. In Proc. 22nd Annual Colloquim on Information Retrieval Reseach, Cambridge, UK, 2000.
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[6] Ron Papka. On-line New Event Detection, Clustering and Tracking. PhD thesis, Department of Computer Science, University of Massachusetts, 1999.
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[7] Ron Papka and James Allan. On-line new event detection using single-pass clustering. Technical Report UMASS Computer Science Technical Report 98 - 21, Department of Computer Science, University of Massachusetts, 1998.
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[8] Yiming Yang, Thomas Ault, Thomas Pierce, and Charles Lattimer. Improving text categorization methods for event detection. In Proc. ACM SIGIR, pages 65-72, 2000.
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[9] Yiming Yang, Jaime Carbonell, Ralf Brown, Thomas Pierce, Brian T. Archibald, and Xin Liu. Learning approaches for detecting and tracking news events. IEEE Intelligent Systems Special Issue on Applications of Intelligent Information Retrieval, 14(4):32 - 43, 1999.
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[10] Yiming Yang, Thomas Pierce, and Jaime Carbonell. A study on retrospective and on-line event detection. In Proc. ACM SIGIR, pages 28-36, Melbourne, 1998.
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