An Information Retrieval Approach for Automatically Constructing Software Libraries
IEEE Transactions on Software Engineering
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
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
Using unlabeled data to improve text classification
Using unlabeled data to improve text classification
Similarity-based word sense disambiguation
Computational Linguistics - Special issue on word sense disambiguation
Using the feature projection technique based on a normalized voting method for text classification
Information Processing and Management: an International Journal
Automatic text categorization by unsupervised learning
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Text categorization using feature projections
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Investigating unsupervised learning for text categorization bootstrapping
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Information Processing and Management: an International Journal
Information and Software Technology
Blog categorization exploiting domain dictionary and dynamically estimated domains of unknown words
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Improving text categorization bootstrapping via unsupervised learning
ACM Transactions on Speech and Language Processing (TSLP)
Text categorization from category name via lexical reference
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Domain-specific sentiment analysis using contextual feature generation
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Directional distributional similarity for lexical inference
Natural Language Engineering
Large-scale hierarchical text classification without labelled data
Proceedings of the fourth ACM international conference on Web search and data mining
A high performance centroid-based classification approach for language identification
Pattern Recognition Letters
Classifying unlabeled short texts using a fuzzy declarative approach
Language Resources and Evaluation
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A wide range of supervised learning algorithms has been applied to Text Categorization. However, the supervised learning approaches have some problems. One of them is that they require a large, often prohibitive, number of labeled training documents for accurate learning. Generally, acquiring class labels for training data is costly, while gathering a large quantity of unlabeled data is cheap. We here propose a new automatic text categorization method for learning from only unlabeled data using a bootstrapping framework and a feature projection technique. From results of our experiments, our method showed reasonably comparable performance compared with a supervised method. If our method is used in a text categorization task, building text categorization systems will become significantly faster and less expensive.