Short Text Understanding by Leveraging Knowledge into Topic Model

Shansong Yang, Weiming Lu, Dezhi Yang, Liang Yao, Baogang Wei
2015 Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  
In this paper, we investigate the challenging task of understanding short text (STU task) by jointly considering topic modeling and knowledge incorporation. Knowledge incorporation can solve the content sparsity problem effectively for topic modeling. Specifically, the phrase topic model is proposed to leverage the auto-mined knowledge, i.e., the phrases, to guide the generative process of short text. Experimental results illustrate the effectiveness of the mechanism that utilizes knowledge to improve topic modeling's performance.
doi:10.3115/v1/n15-1131 dblp:conf/naacl/YangLYYW15 fatcat:bfs2gndaa5bplfamafa2plc4oi