Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Modern Information Retrieval
Ontologies Improve Text Document Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Enhancing text clustering by leveraging Wikipedia semantics
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Building semantic kernels for text classification using wikipedia
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Documents Using a Wikipedia-Based Concept Representation
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Wikipedia-based semantic interpretation for natural language processing
Journal of Artificial Intelligence Research
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection
IEEE Transactions on Neural Networks
Text clustering based on granular computing and wikipedia
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
| Hi-index | 0.00 |
Text categorization is one of the most common themes in data mining and machine learning fields. Unlike structured data, unstructured text data is more complicated to be analyzed because it contains too much information, e.g., syntactic and semantic. In this paper, we propose a semantics-based model to represent text data in two levels. One level is for syntactic information and the other is for semantic information. Syntactic level represents each document as a term vector, and the component records tf-idf value of each term. The semantic level represents document with Wikipedia concepts related to terms in syntactic level. The syntactic and semantic information are efficiently combined by our proposed multi-layer classification framework. Experimental results on benchmark dataset (Reuters-21578) have shown that the proposed representation model plus proposed classification framework improves the performance of text classification by comparing with the flat text representation models (term VSM, concept VSM, term+concept VSM) plus existing classification methods.