Jin Fenglei


An Online Topic Modeling Framework with Topics Automatically Labeled
Jin Fenglei | Gao Cuiyun | Lyu Michael R.
Proceedings of the 2019 Workshop on Widening NLP

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. Experiments on 7,076 Stack Exchange posts show the effectiveness of IEDL in tracking topic changes.