Abstract
A large number of electronic documents are labeled using human-interpretable annotations. High-efficiency text mining on such data set requires generative model that can flexibly comprehend the significance of observed labels while simultaneously uncovering topics within unlabeled documents. This paper presents a novel and generalized on-line labeled topic model (OLT) tracking the time development of extracted topics through a structured multi-labeled data set. Our topic model has an incrementally updated principle based on time slices in an on-line fashion, and can detect dynamic trending for labeled topics in parallel. Empirical results are presented to demonstrate lower perplexity and high perfor- mance of our proposed model when compared with other models.
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