Collection of Articles On Text Categorization

Section 0 Rennie, Rifkin: Improving Multiclass Text Classification with the Support Vector Machine (Oct. 2001) (using 20 Newsgroups Data Set) Georges Siolas, Florence d'Alche-Buc: Support Vector Machines based on a Semantic Kernel for Text Categorization (using 20 Newsgroups Data Set) Burges: A Tutorial on Support Vector Machines Osuna et al.: Support Vector Machines, Training and Applications Ngai Tang: Text Categorisation using Support Vector Machines (interesting dissertation, 30 August 2001) Section 1 Domingos, Pazzani: On the Optimality of the Simple Bayesian Classifier und Zero-One Loss Fabrizio Sebastiani: A Tutorial on Automated Text Categorisation Fabrizio Sebastiani: Machine Learning in Automated Text Categorization Fabrizio Sebastiani: Machine Learning in Automated Text Categorization (differently formatted, i.e. 55 pages instead of 63) Galavotti, Sebastiani, Simi: Experiments on the Use of Feature Selection and Negative Evidence in Automated Text Categorization Automatic Web Page Categorization by Link and Context Analysis Categorisation by Context Guest Editors'Introduction to the Special Issue on Automated Text Categorization Caropreso, Matwin, Sebastiani: A Learner-Independent Evaluation of the Usefulness of Statistical Phrases for Automated Text Categorization Lewis et al: Naive (Bayes) at Fourty Section 2 Jason D.M. Rennie: Improving Multi-class Text Classification with Naive Bayes (Master's Thesis) McCallum, Nigam, Rennie, Seymore: A Machine Learning Approach to Building Domain-Specific Search Engines Nigam, McCallum, Thrun, Mitchell: Learning to Classify Text from Labeled and Unlabeled Documents Nigam, McCallum, Thrun, Mitchell: Learning to Classify Text from Labeled and Unlabeled Documents (condensed version) McCallum, Nigam: A Comparison of Event Models for Naive Bayes Text Classification McCallum: Multi-Label Text Classification with a Mixture Model Trained by EMn McCallum, Nigam: Employing EM and Pool-Based Active Learning for Text Classification Craven, DiPasquo, Freitag, McCallum, Mitchell, Nigam, Slattery: Learning to Extract Symbolic Knowledge from the WWW Baker, McCallum: Distributional Clustering of Words for Text Classification (newer?) Baker, McCallum: Distributional Clustering of Words for Text Classification Using Maximum Entropy for Text Classificationn Andrew McCallum, Fernando Freitag: Maximum Entropy Markow Models for Information Extraction and Segmentation D'Alessio, Murray, Schiaffino: The Effect of Using Hierarchical Classifiers in Text Categorization Section 3 David Yarowsky: Word-Sense Disambiguation, Using Statistical Models of Roget's Categories, Trained on Large Corpora Ide, Veronis: Word Sense Disambiguation: The State of the Art Schütze: Automatic Word Sense Discrimination Mladenic, Grobelnik: Word Sequences as Features in Text-Learning Yang et. al.: Learning Approaches for Detecting and Tracking News Events Section 4 Apte, Damerau, Weiss: Automated Learning of Decision Rules for Text Categorization Susan Dumais, Hao Chen: Hierarchical Classification of Web Content

Section 5 Lewis, Jones: Natural Language Processing for Information Retrieval Wiener, Pedersen, Weigend: A Neural Network Approach to Topic Spotting Gorniak, Peter: Sorting Email Messages by Topic Gorniak, Peter: MailMind, A Connectionist E-Mail Sorting Client Section 6 Vijay Boyapati: Towards a Comprehensive Topic, Hierarchy for News Moulinier, Raskinis, Ganascia: Text Categorization: a Symbolic Approach Quasthoff, Wolff: Effizientes Dokumentclustering durch niederfrequente Therme Section 7 Yang, Pederson: A Comparative Study on Feature Selection in Text Categorization Yang, Liu: A re-examination of Text Categorisation Methods Improving Text Classification by Shrinkage in a Hierarchy of Classes John, Kohavi, Pfleger: Irrelevant Features and the Subset Selection Problem Martijn Spitters: Comparing feature sets for learning text categorization Ellen Riloff: Little Words Can Make a Big Difference Fuka, Hanka: Feature Set Reductuction for Document Classification Problems Feature subset selection in text-learning Ruiz, Srinivasan: Hierarchical Neural Networks for Text Categorization Section 8 (some only in print) An Algorithm for Suffix Stripping Hsu, Lang: Feature Reduction and Database Maintanance in NETNEWS Classification Thomas Hofmann: Learning and Representing Topic Seminararbeit: Advanced Information Retrieval Methods Section 9 Sam Scott: Feature Engineering for a Symbolic Approach to Text Classification Kermit, et al.: Automatic Complexity Management: Personalised Document Retrieval from the World Wide Webn Michie, et. al.: Machine Learning, Neural and Statistical Classification ( 298 pages!, review of different approaches to text classification) Joachims: A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization Meghini et. al.: A Model of Multimedia Information Retrieval Slonim, Tishby: Document Clustering using Word Clusters via the Information Bottleneck Method Mlademic: Turning Yahoo into an Automatic Web-Page Classifier Mlademic, Grobelnik:Assigning keywords to documents using machine learning Fuhr et. al.: AIR/X a Rule-Based Multistage Indexing System for Large Subject _Fields Mitchell: Machine Learning, Slides for instructors Various Articles by Junker Mladenic: Text-Learning and Related Intelligent Agents: A Survey Compression: A Key for Next-Generation Text Retrieval Systems Chang: Enabling Concept-Based Relevance Feedback for Information Retrieval on the WWW