Improving Topic Model Stability for Effective Document Exploration
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4223-4226.
Abstract
Topic modeling has become a ubiquitous topic analysis tool for text exploration. Most of the existing works on topic modeling focus on fitting topic models to input data. They however ignore an important usability issue that is closely related to the end user experience: stability. In this study, we investigate the stability problem in topic modeling. We first report on the experiments conducted to quantify the severity of the problem. We then propose a new learning framework to mitigate the problem by explicitly incorporating topic stability constraints in model training. We also perform user study to demonstrate the advantages of the proposed method.
@inproceedings{ijcai2016p635,
title = {Improving Topic Model Stability for Effective Document Exploration},
author = {Yang, Yi and Pan, Shimei and Song, Yangqiu and Lu, Jie and Topkara, Mercan},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {4223--4226},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/635},
}
TY - CPAPER TI - Improving Topic Model Stability for Effective Document Exploration AU - Yang, Yi AU - Pan, Shimei AU - Song, Yangqiu AU - Lu, Jie AU - Topkara, Mercan T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 4223 EP - 4226 UR - https://ijcai.org/proceedings/2016/635 ER -Copied
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