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Probabilistic Word Selection via Topic Modeling
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DOI：10.1109/TKDE.2014.2377727.png)
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Abstract
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We propose selective supervised Latent Dirichlet Allocation (ssLDA) to boost the prediction performance of the widely studied supervised probabilistic topic models. We introduce a Bernoulli distribution for each word in one given document to select this word as a strongly or weakly discriminative one with respect to its assigned topic. The Bernoulli distribution is parameterized by the discrimination power of the word for its assigned topic. As a result, the document is represented as a bag-of-selective-words instead of the probabilistic bag-of-topics in the topic modeling domain or the flat bag-of-words in the traditional natural language processing domain to form a new perspective. Inheriting the general framework of supervised LDA (sLDA), ssLDA can also predict many types of response specified by a Gaussian Linear Model (GLM). Focusing on the utilization of this word selection mechanism for singe-label document classification in this paper, we conduct the variational inference for approximating the intractable posterior and derive a maximum-likelihood estimation of parameters in ssLDA. The experiments reported on textual documents show that ssLDA not only performs competitively over state-of-the-art classification approaches based on both the flat bag-of-words and probabilistic bag-of-topics representation in terms of classification performance, but also has the ability to discover the discrimination power of the words specified in the topics (compatible with our rational knowledge).
Keywords:
Topic modeling
latent Dirichlet allocation
supervised learning
classification
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