Probabilistic Word Selection via Topic Modeling

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Abstract—

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).

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