Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Probabilistic latent semantic indexing
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Machine Learning - Special issue on information retrieval
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SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A semisupervised learning method to merge search engine results
ACM Transactions on Information Systems (TOIS)
Semi-supervised learning with explicit misclassification modeling
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MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Learning aspect models with partially labeled data
Pattern Recognition Letters
Large-scale hierarchical text classification without labelled data
Proceedings of the fourth ACM international conference on Web search and data mining
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ACM Transactions on Internet Technology (TOIT)
An extension of the aspect PLSA model to active and semi-supervised learning for text classification
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
A hybrid semi-supervised topic model
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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This paper investigates a new extension of the Probabilistic Latent Semantic Analysis (PLSA) model [6] for text classification where the training set is partially labeled. The proposed approach iteratively labels the unlabeled documents and estimates the probabilities of its labeling errors. These probabilities are then taken into account in the estimation of the new model parameters before the next round. Our approach outperforms an earlier semi-supervised extension of PLSA introduced by [9] which is based on the use of fake labels. However, it maintains its simplicity and ability to solve multiclass problems. In addition, it gives valuable information about the most uncertain and difficult classes to label. We perform experiments over the 20Newsgroups, WebKB and Reuters document collections and show the effectiveness of our approach over two other semi-supervised algorithms applied to these text classification problems.