Discovery of numerous specific topics via term co-occurrence analysis

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. Categories and Subject Descriptors H.3.3 [Information Systems]: Information Search and Retrieval--Clustering General Terms Algorithms Keywords unsupervised learning, text mining, ...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Omid Madani, Jiye Yu
• Keywords: CIKM 2010, Information Technology, Large Term Co-occurrence, Layered Pyramidal View, Small Dense Subgraph