Dataless text classification with descriptive LDA

Chen, Xingyuan, Xia, Yunqing, Jin, Peng and Carroll, John (2015) Dataless text classification with descriptive LDA. 29th AAAI Conference on Artificial Intelligence (AAAI-15), Austin, Texas, USA, January 25–30, 2015. Published in: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. 3 2224-2231. Association for the Advancement of Artificial Intelligence Press ISBN 9781577357018

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Manually labeling documents for training a text classifier is expensive and time-consuming. Moreover, a classifier trained on labeled documents may suffer from overfitting and adaptability problems. Dataless text classification (DLTC) has been proposed as a solution to these problems, since it does not require labeled documents. Previous research in DLTC has used explicit semantic analysis of Wikipedia content to measure semantic distance between documents, which is in turn used to classify test documents based on nearest neighbours. The semantic-based DLTC method has a major drawback in that it relies on a large-scale, finely-compiled semantic knowledge base, which is difficult to obtain in many scenarios. In this paper we propose a novel kind of model, descriptive LDA (DescLDA), which performs DLTC with only category description words and unlabeled documents. In DescLDA, the LDA model is assembled with a describing device to infer Dirichlet priors from prior descriptive documents created with category description words. The Dirichlet priors are then used by LDA to induce category-aware latent topics from unlabeled documents. Experimental results with the 20Newsgroups and RCV1 datasets show that: (1) our DLTC method is more effective than the semantic-based DLTC baseline method; and (2) the accuracy of our DLTC method is very close to state-of-the-art supervised text classification methods. As neither external knowledge resources nor labeled documents are required, our DLTC method is applicable to a wider range of scenarios.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Depositing User: John Carroll
Date Deposited: 23 Apr 2015 14:33
Last Modified: 23 Oct 2020 14:24

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