Ensemble topic modeling using weighted term co-associations
作者：
Highlights：
• Issues of evaluating and comparing topic modeling solutions are surveyed.
• An interpretable ensemble topic modeling approach has been proposed to solve this.
• This approach uses term co-associations and semantic similarity information.
• This allows for visualisation of topical structure, ranking of topics and term pairs.
• A detailed evaluation is performed on 27 datasets, with 3 background corpora.
摘要
•Issues of evaluating and comparing topic modeling solutions are surveyed.•An interpretable ensemble topic modeling approach has been proposed to solve this.•This approach uses term co-associations and semantic similarity information.•This allows for visualisation of topical structure, ranking of topics and term pairs.•A detailed evaluation is performed on 27 datasets, with 3 background corpora.
论文关键词：Topic modeling,Ensemble learning,Evaluation,Word embeddings,Interpretation
论文评审过程：Received 24 February 2020, Revised 17 June 2020, Accepted 29 June 2020, Available online 8 July 2020, Version of Record 21 July 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113709
