Weakly supervised prototype topic model with discriminative seed words: modifying the category prior by self-exploring supervised signals

• Published in: Soft computing (Berlin, Germany) Vol. 27; no. 9; pp. 5397 - 5410
• Main Authors: Li, Ximing, Wang, Bing, Wang, Yue, Ouyang, Jihong, Garg, Harish, Thanh, Dang N. H.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Data Analytics and Machine Learning; Engineering; Mathematical Logic and Foundations; Mechatronics; Robotics; Topic modeling; Dataless text classification; Prototype scheme; Seed words; Category prior
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-022-07771-9

Dataless text classification, i.e., a new paradigm of weakly supervised learning, refers to the task of learning with unlabeled documents and a few predefined representative words of categories, known as seed words . The recent generative dataless methods construct document-specific category priors by using seed word occurrences only; however, such category priors often contain very limited and even noisy supervised signals. To remedy this problem, in this paper, we propose a novel formulation of category prior. First, for each document, we consider its label membership degree by not only counting seed word occurrences, but also using a novel prototype scheme , which captures pseudo-nearest neighboring categories. Second, for each label, we consider its frequency prior knowledge of the corpus, which is also a discriminative knowledge for classification. By incorporating the proposed category prior into the previous generative dataless method, we suggest a novel generative dataless method, namely Weakly Supervised Prototype Topic Model. The experimental results on real-world datasets demonstrate that W sptm outperforms the existing baseline methods.