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Mining a set of meaningful and distinctive topics automatically from massivetext corpora has broad applications. Existing topic models, however, typicallywork in a purely unsupervised way, which often generate topics that do not fitusers' particular needs and yield suboptimal performance on downstream tasks.We propose a new task, discriminative topic mining, which leverages a set ofuser-provided category names to mine discriminative topics from text corpora.This new task not only helps a user understand clearly and distinctively thetopics he/she is most interested in, but also benefits directly keyword-drivenclassification tasks. We develop CatE, a novel category-name guided textembedding method for discriminative topic mining, which effectively leveragesminimal user guidance to learn a discriminative embedding space and discovercategory representative terms in an iterative manner. We conduct acomprehensive set of experiments to show that CatE mines high-quality set oftopics guided by category names only, and benefits a variety of downstreamapplications including weakly-supervised classification and lexical entailmentdirection identification.

Discriminative Topic Mining via Category-Name Guided Text Embedding