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Publication in EndNote Format

%0 Conference Proceedings %A Madani, O. and Yu, J. %T Discovery of Numerous Specific Topics via Term Co-occurrence Analysis %B Conference on Information and Knowledge Management (CIKM) %D 2010 %K unsupervised learning, text mining, co-occurrence graphs, topic discovery, tagging, feature induction, feature augmentation %X We describe efficient techniques for construction of large term co-occurrence graphs, and investigate an application to the discovery of numerous fine-grained (specific) topics. A topic is a small dense subgraph discovered by a random walk initiated at a term (node) in the graph. We observe that the discovered topics are highly interpretable, and reveal the different meanings of terms in the corpus. We show the information-theoretic utility of the topics when they are used as features in supervised learning. Such features lead to consistent improvements in classification accuracy over the standard bag-of-words representation, even at high training proportions. We explain how a layered pyramidal view of the term distribution helps in understanding the algorithms and in visualizing and interpreting the topics. %U http://www.ai.sri.com/pubs/files/1806.pdf

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