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