Labeled Phrase Latent Dirichlet Allocation and its online learning algorithm

There is a mass of user-marked text data on the Internet, such as web pages with categories, papers with corresponding keywords, and tweets with hashtags. In recent years, supervised topic models, such as Labeled Latent Dirichlet Allocation, have been widely used to discover the abstract topics in labeled text corpora. However, none of these topic models have taken into consideration word order under the bag-of-words assumption, which will obviously lose a lot of semantic information. In this paper, in order to synchronously model semantical label information and word order, we propose a novel topic model, called Labeled Phrase Latent Dirichlet Allocation (LPLDA), which regards each document as a mixture of phrases and partly considers the word order. In order to obtain the parameter estimation for the proposed LPLDA model, we develop a batch inference algorithm based on Gibbs sampling technique. Moreover, to accelerate the LPLDA's processing speed for large-scale stream data, we further propose an online inference algorithm for LPLDA. Extensive experiments were conducted among LPLDA and four state-of-the-art baselines. The results show (1) batch LPLDA significantly outperforms baselines in terms of case study, perplexity and scalability, and the third party task in most cases; (2) the online algorithm for LPLDA is obviously more efficient than batch method under the premise of good results.