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

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 cla...

Full description

Saved in:
Bibliographic Details
Published in:Natural language engineering 2022-01, Vol.28 (1), p.39-69
Main Authors: Jin, Yiping, Wanvarie, Dittaya, Le, Phu T. V.
Format: Article
Language:English
Subjects:
Accuracy
Advertising
Classification
Classifiers
Datasets
Documents
Domains
Keywords
Machine learning
Noise
Statistical analysis
Statistical methods
Taxonomy
Text categorization
User generated content
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1351-3249
1469-8110
DOI:10.1017/S1351324920000340