Data & Knowledge Engineering
Overview and semantic issues of text mining
ACM SIGMOD Record
H-BayesClust: A New Hierarchical Clustering Based on Bayesian Networks
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
A multi-view approach to semi-supervised document classification with incremental Naive Bayes
Computers & Mathematics with Applications
Multi-focal learning and its application to customer service support
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining web query hierarchies from clickthrough data
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
Learning domain ontologies for semantic Web service descriptions
Web Semantics: Science, Services and Agents on the World Wide Web
Web Semantics: Science, Services and Agents on the World Wide Web
Multifocal learning for customer problem analysis
ACM Transactions on Intelligent Systems and Technology (TIST)
Transductive learning for text classification using explicit knowledge models
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Concept based text classification using labeled and unlabeled data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
PROBABILISTIC HEURISTICS FOR HIERARCHICAL WEB DATA CLUSTERING
Computational Intelligence
Formalizing the get-specific document classification algorithm
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
The dictionary-based quantified conceptual relations for hard and soft Chinese text clustering
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
Abstracting for Dimensionality Reduction in Text Classification
International Journal of Intelligent Systems
Semantic to intelligent web era: building blocks, applications, and current trends
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
Hi-index | 0.00 |
Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.