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Dataless text classification with descriptive LDA
conference contribution
posted on 2023-06-08, 20:34 authored by Xingyuan Chen, Yunqing Xia, Peng Jin, John CarrollManually 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.
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Publication status
- Published
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- Published version
Journal
Proceedings of the Twenty-Ninth AAAI Conference on Artificial IntelligencePublisher
Association for the Advancement of Artificial Intelligence PressVolume
3Page range
2224-2231Event name
29th AAAI Conference on Artificial Intelligence (AAAI-15)Event location
Austin, Texas, USAEvent type
conferenceEvent date
January 25–30, 2015ISBN
9781577357018Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2015-04-23First Open Access (FOA) Date
2015-04-23First Compliant Deposit (FCD) Date
2015-04-23Usage metrics
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