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
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Athena: Mining-Based Interactive Management of Text Database
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Cross-training: learning probabilistic mappings between topics
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Separating Style and Content with Bilinear Models
Neural Computation
| Hi-index | 0.00 |
Many text documents naturally have two kinds of labels. For example, we may label web pages from universities according to their categories, such as “student” or “faculty”, or according the source universities, such as “Cornell” or “Texas”. We call one kind of labels the content and the other kind the style. Given a set of documents, each with both content and style labels, we seek to effectively learn to classify a set of documents in a new style with no content labels into its content classes. Assuming that every document is generated using words drawn from a mixture of two multinomial component models, one content model and one style model, we propose a method named Cartesian EM that constructs content models and style models through Expectation Maximization and performs classification of the unknown content classes transductively. Our experiments on real-world datasets show the proposed method to be effective for style independent text content classification.