Sparse Supervised Topic Model: Midterm Report
نویسندگان
چکیده
In this paper we propose the sparse supervised topic model (SSTM), a graphical model that learns topic structures of a given document collection and also a sparse linear prediction model for response vairables associated with documents. Our model jointly learns the topics and the classifier and encourages a sparse classifier by concentrating all the relevant information for prediction into a small set of topics. Experimental results show that our proposed SSTM model has good interpretability on both classification and regression tasks while still achieves reasonable performance in terms of prediction accuracy.
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