Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Distributional word clusters vs. words for text categorization
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Does a new simple Gaussian weighting approach perform well in text categorization?
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Automatic Text Categorization (TC) is a complex and useful task for many natural language applications, and is usually performed by using a set of manually classified documents, i.e. a training collection. Term-based representation of documents has found widespread use in TC. However, one of the main shortcomings of such methods is that they largely disregard lexical semantics and, as a consequence, are not sufficiently robust with respect to variations in word usage. In this paper we design, implement, and evaluate a new text classification technique. Our main idea consists in finding a series of projections of the training data by using a new, modified LSI algorithm, projecting all training instances to the low-dimensional subspace found in the previous step, and finally inducing a binary search on the projected low-dimensional data. Our conclusion is that, with all its simplicity and efficiency, our approach is comparable to SVM accuracy on classification.