Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Blog search and mining in the business domain
Proceedings of the 2007 international workshop on Domain driven data mining
Machine learning techniques for business blog search and mining
Expert Systems with Applications: An International Journal
Combining named entities and tags for novel sentence detection
Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval
Sentence-Level Novelty Detection in English and Malay
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Design and development of a mobile peer-to-peer social networking application
Expert Systems with Applications: An International Journal
Blended metrics for novel sentence mining
Expert Systems with Applications: An International Journal
Evaluation of novelty metrics for sentence-level novelty mining
Information Sciences: an International Journal
Probabilistic techniques for corporate blog mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Multilingual novelty detection
Expert Systems with Applications: An International Journal
Dimensionality reduction techniques for blog visualization
Expert Systems with Applications: An International Journal
Authorship Identification for Online Text
CW '10 Proceedings of the 2010 International Conference on Cyberworlds
An intelligent system for sentence retrieval and novelty mining
International Journal of Knowledge Engineering and Data Mining
A tag-topic model for blog mining
Expert Systems with Applications: An International Journal
Multilingual sentence categorization and novelty mining
Information Processing and Management: an International Journal
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
D2S: Document-to-sentence framework for novelty detection
Knowledge and Information Systems
International Journal of Advanced Pervasive and Ubiquitous Computing
A data-centric approach to feed search in blogs
International Journal of Web Engineering and Technology
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Blog tags are labels of blog documents that classify them into different categories. Most tags are user-generated, which create problems such as inconsistencies in tags across different users, blogs without tags, lack of descriptive tags, lack of semantic distinction, etc. In this paper, we utilise dimensionality reduction techniques to reduce the inherent noise in blog tags. A tag-topic model is combined with dimensionality reduction, and then evaluated on real-world blog data. By employing dimensionality reduction techniques to reduce the document-tag space, better classification results were achieved. This indicates that the noise in tags can be effectively reduced by representing the original set of tags with a smaller number of latent tags, which can lead to more accurate real-time categorisation of blog documents.