Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Proceedings of the 13th international conference on World Wide Web
PRINCIPAR: an efficient, broad-coverage, principle-based parser
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
HARP: A Practical Projected Clustering Algorithm
IEEE Transactions on Knowledge and Data Engineering
Learn from web search logs to organize search results
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A knowledge-based search engine powered by wikipedia
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Inducing word senses to improve web search result clustering
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Clustering web search results with maximum spanning trees
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Social network analysis and mining to support the assessment of on-line student participation
ACM SIGKDD Explorations Newsletter
Improved query suggestion by query search
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
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Effectively organizing web search results into clusters is important to facilitate quick user navigation to relevant documents. Previous methods may rely on a training process and do not provide a measure for whether page clustering is actually required. In this paper, we reformalize the clustering problem as a word sense discovery problem. Given a query and a list of result pages, our unsupervised method detects word sense communities in the extracted keyword network. The documents are assigned to several refined word sense communities to form clusters. We use the modularity score of the discovered keyword community structure to measure page clustering necessity. Experimental results verify our method's feasibility and effectiveness.