An Online Topic Modeling Framework with Topics Automatically Labeled

Jin Fenglei, Gao Cuiyun, Lyu Michael R.


Abstract
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.
Anthology ID:
W19-3624
Volume:
Proceedings of the 2019 Workshop on Widening NLP
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS | WiNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
73–76
Language:
URL:
https://www.aclweb.org/anthology/W19-3624
DOI:
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