Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature Engineering for Text Classification
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Text Classification by Boosting Weak Learners based on Terms and Concepts
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
The database research group at the Max-Planck Institute for Informatics
ACM SIGMOD Record
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Fast logistic regression for text categorization with variable-length n-grams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Knowledge Supervised Text Classification with No Labeled Documents
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Expectation maximization enhancement with evolutionstrategy for stochastic ontology mapping
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Towards better ontological support for recognizing textual entailment
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
A document is known by the company it keeps: neighborhood consensus for short text categorization
Language Resources and Evaluation
| Hi-index | 0.00 |
We present a generative model based approach for transductive learning for text classification. Our approach combines three methodological ingredients: learning from background corpora, latent variable models for decomposing the topic-word space into topic-concept and concept-word spaces, and explicit knowledge models (light-weight ontologies, thesauri, e.g. WordNet) with named concepts for populating latent variables. The combination has synergies that can boost the combined performance. This paper presents the theoretical model and extensive experimental results on three data collections. Our experiments show improved classification results over state-of-the-art classification techniques such as the Spectral Graph Transducer and Transductive Support Vector Machines, particularly for the case of sparse training.