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To date, there have been massive Semi-Structured Documents (SSDs) during theevolution of the Internet. These SSDs contain both unstructured features (e.g.,plain text) and metadata (e.g., tags). Most previous works focused on modelingthe unstructured text, and recently, some other methods have been proposed tomodel the unstructured text with specific tags. To build a general model forSSDs remains an important problem in terms of both model fitness andefficiency. We propose a novel method to model the SSDs by a so-calledTag-Weighted Topic Model (TWTM). TWTM is a framework that leverages both thetags and words information, not only to learn the document-topic and topic-worddistributions, but also to infer the tag-topic distributions for text miningtasks. We present an efficient variational inference method with an EMalgorithm for estimating the model parameters. Meanwhile, we propose threelarge-scale solutions for our model under the MapReduce distributed computingplatform for modeling large-scale SSDs. The experimental results show theeffectiveness, efficiency and the robustness by comparing our model with thestate-of-the-art methods in document modeling, tags prediction and textclassification. We also show the performance of the three distributed solutionsin terms of time and accuracy on document modeling.

Tag-Weighted Topic Model For Large-scale Semi-Structured Documents