Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Modern Information Retrieval
The Journal of Machine Learning Research
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
Clustering short texts using wikipedia
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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
Improving Text Classification by Using Encyclopedia Knowledge
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Clustering Documents with Active Learning Using Wikipedia
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Expert Systems with Applications: An International Journal
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
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Importance of semantic representation: dataless classification
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
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
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection
IEEE Transactions on Neural Networks
A robust video text detection approach using SVM
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Text categorization is one of the most common themes in data mining and machine learning fields. Unlike structured data, unstructured text data is more difficult to be analyzed because it contains complicated both syntactic and semantic information. In this paper, we propose a two-level representation model (2RM) to represent text data, one is for representing syntactic information and the other is for semantic information. Each document, in syntactic level, is represented as a term vector where the value of each component is the term frequency and inverse document frequency. The Wikipedia concepts related to terms in syntactic level are used to represent document in semantic level. Meanwhile, we designed a multi-layer classification framework (MLCLA) to make use of the semantic and syntactic information represented in 2RM model. The MLCLA framework contains three classifiers. Among them, two classifiers are applied on syntactic level and semantic level in parallel. The outputs of these two classifiers will be combined and input to the third classifier, so that the final results can be obtained. Experimental results on benchmark data sets (20Newsgroups, Reuters-21578 and Classic3) have shown that the proposed 2RM model plus MLCLA framework improves the text classification performance by comparing with the existing flat text representation models (Term-based VSM, Term Semantic Kernel Model, Concept-based VSM, Concept Semantic Kernel Model and Term+Concept VSM) plus existing classification methods.