Automatic text processing
Information Processing and Management: an International Journal - Special issue: history of information science
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring the similarity space
ACM SIGIR Forum
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
On feature distributional clustering for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Effective site finding using link anchor information
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Retrieval
Compression of inverted indexes For fast query evaluation
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Strategies for minimising errors in hierarchical web categorisation
Proceedings of the eleventh international conference on Information and knowledge management
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Indexing for fast categorisation
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Efficient single-pass index construction for text databases
Journal of the American Society for Information Science and Technology
Fast and accurate text classification via multiple linear discriminant projections
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Categorisation is a useful method for organising documents into subcollections that can be browsed or searched to more accurately and quickly meet information needs. On the Web, category-based portals such as Yahoo! and DMOZ are extremely popular: DMOZ is maintained by over 56,000 volunteers, is used as the basis of the popular Google directory, and is perhaps used by millions of users each day. Support Vector Machines (SVM) is a machine-learning algorithm which has been shown to be highly effective for automatic text categorisation. However, a problem with iterative training techniques such as SVM is that during their learning or training phase, they require the entire training collection to be held in main-memory; this is infeasible for large training collections such as DMOZ or large news wire feeds. In this paper, we show how inverted indexes can be used for scalable training in categorisation, and propose novel heuristics for a fast, accurate, and memory efficient approach. Our results show that an index can be constructed on a desktop workstation with little effect on categorisation accu-racy compared to a memory-based approach. We conclude that our techniques permit automatic categorisation using very large train-ing collections, vocabularies, and numbers of categories.