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Learning from noisy out-of-domain corpus using dataless classification

Published online by Cambridge University Press:  17 June 2020

Yiping Jin Open the ORCID record for Yiping Jin [Opens in a new window] ,
Dittaya Wanvarie  and
Phu T. V. Le
Yiping Jin
Affiliation:
Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok10300, Thailand
Dittaya Wanvarie*
Affiliation:
Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok10300, Thailand
Phu T. V. Le
Affiliation:
Knorex Pte. Ltd., 8 Cross St, Singapore 048424, Singapore
*
*Corresponding author. E-mail: [email protected]
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Abstract

In real-world applications, text classification models often suffer from a lack of accurately labelled documents. The available labelled documents may also be out of domain, making the trained model not able to perform well in the target domain. In this work, we mitigate the data problem of text classification using a two-stage approach. First, we mine representative keywords from a noisy out-of-domain data set using statistical methods. We then apply a dataless classification method to learn from the automatically selected keywords and unlabelled in-domain data. The proposed approach outperformed various supervised learning and dataless classification baselines by a large margin. We evaluated different keyword selection methods intrinsically and extrinsically by measuring their impact on the dataless classification accuracy. Last but not least, we conducted an in-depth analysis of the behaviour of the classifier and explained why the proposed dataless classification method outperformed supervised learning counterparts.

Keywords

Text classificationDataless classificationNoisy labelsDomain adaptation
Type
Article
Information
Natural Language Engineering , Volume 28 , Issue 1 , January 2022 , pp. 39 - 69
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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