Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
Ontology Learning and Its Application to Automated Terminology Translation
IEEE Intelligent Systems
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
On Machine Learning Methods for Chinese Document Categorization
Applied Intelligence
Automatic Textual Document Categorization Based on Generalized Instance Sets and a Metamodel
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
Ontology Based Semantic Similarity Comparison of Documents
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
On Using Partial Supervision for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Blocking Reduction Strategies in Hierarchical Text Classification
IEEE Transactions on Knowledge and Data Engineering
Text classification with kernels on the multinomial manifold
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
On Combining Classifier Mass Functions for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to XML path matching
Proceedings of the 9th annual ACM international workshop on Web information and data management
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This paper firstly utilizes the ontology such as WordNet to build the semantic structures of text documents, and then enhance the semantic similarity among them Because the correlations between documents make them lie on or close to a smooth low-dimensional manifold so that documents can be well characterized by a manifold within the space of documents, we calculate the similarity between any two semantically structured documents with respect to the intrinsic global manifold structure This idea has been validated in the conducted text categorization experiments on patent documents.