A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
PageRank without hyperlinks: structural re-ranking using links induced by language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Generative model-based document clustering: a comparative study
Knowledge and Information Systems
Clustering short texts using wikipedia
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Enhancing text clustering by leveraging Wikipedia semantics
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Improving Text Classification by Using Encyclopedia Knowledge
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Query dependent pseudo-relevance feedback based on wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Enhancing cluster labeling using wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
WordNet-based text document clustering
ROMAND '04 Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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The conventional algorithms for text clustering that are based on the bag of words model, fail to fully capture the semantic relations between the words. As a result, documents describing an identical topic may not be categorized into same clusters if they use different sets of words. A generic solution for this issue is to utilize background knowledge to enrich the document contents. In this research, we adopt a language modeling approach for text clustering and propose to smooth the document language models using Wikipedia articles in order to enhance text clustering performance. The contents of Wikipedia articles as well as their assigned categories are used in three different ways to smooth the document language models with the goal of enriching the document contents. Clustering is then performed on a document similarity graph constructed on the enhanced document collection. Experiment results confirm the effectiveness of the proposed methods.