  In this paper, we propose a novel online topic tracking framework, named
IEDL, for tracking the topic changes related to deep learning techniques on
Stack Exchange and automatically interpreting each identified topic. The
proposed framework combines the prior topic distributions in a time window
during inferring the topics in current time slice, and introduces a new ranking
scheme to select most representative phrases and sentences for the inferred
topics in each time slice. Experiments on 7,076 Stack Exchange posts show the
effectiveness of IEDL in tracking topic changes and labeling topics.
