S2vNTM: Semi-supervised vMF Neural Topic Modeling
This addresses the need for more efficient and knowledge-integrated topic modeling methods, though it appears incremental as it builds on existing semi-supervised approaches.
The paper tackles the problem of integrating human knowledge and reducing resource needs in topic modeling by proposing S2vNTM, which uses seed keywords to improve topic identification and optimization, resulting in higher classification accuracy and at least twice the speed of baselines.
Language model based methods are powerful techniques for text classification. However, the models have several shortcomings. (1) It is difficult to integrate human knowledge such as keywords. (2) It needs a lot of resources to train the models. (3) It relied on large text data to pretrain. In this paper, we propose Semi-Supervised vMF Neural Topic Modeling (S2vNTM) to overcome these difficulties. S2vNTM takes a few seed keywords as input for topics. S2vNTM leverages the pattern of keywords to identify potential topics, as well as optimize the quality of topics' keywords sets. Across a variety of datasets, S2vNTM outperforms existing semi-supervised topic modeling methods in classification accuracy with limited keywords provided. S2vNTM is at least twice as fast as baselines.