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Latent Dirichlet allocation - Wikipedia, the free encyclopedia
In statistics , latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei , Andrew Ng , and Michael Jordan in 2002. [ 1 ] [ edit ] Topics in LDAThis paper studies web object classification problem with the novel exploration of social tags. Automatically classifying web objects into manageable semantic categories has long been a fundamental preprocess for indexing, browsing, searching, and mining these objects. The explosive growth of heterogeneous web objects, especially non-textual objects such as products, pictures, and videos, has made the problem of web classification increasingly challenging. Such objects often suffer from a lack of easy-extractable features with semantic information, interconnections between each other, as well as training examples with category labels. In this paper, we explore the social tagging data to bridge this gap. We cast web object classification problem as an optimization problem on a graph of objects and tags.
Exploring Social Tagging Graph for Web Object Classification
· Special Issue on Learning to Rank for IR , Information Retrieval Journal , Hang Li, Tie-Yan Liu, Cheng Xiang Zhai, T. Joachims, Springer, 2009. · Special Issue on Automated Text Categorization , Journal on Intelligent Information Systems , T. Joachims and F.
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