NDLI: Hierarchical classification with a topic taxonomy via LDA

Content Provider	Springer Nature Link
Author	He, Li Jia, Yan Ding, Zhaoyun Han, Weihong
Copyright Year	2013
Abstract	Large scale hierarchical classification problem researches how to classify documents into a predefined taxonomy with thousands of categories. As the skewed category distribution over documents, that is, most categories have very few labeled documents, the data sparseness problem in the rare categories lead to a low classification performance. In this paper, we study the problem of web-page classification over the topic taxonomy of the DMOZ directory. For this hard task, we proposed a hierarchical classification model based on Latent Dirichlet allocation (LDA). We use LDA model as the feature extraction technique to extract latent topics to reduce the effects of data sparseness, and construct topic feature vectors associated with the corpus for training more robust classification models for rare categories. Experiments were conducted on the dataset of web pages from the Chinese Simplified branch of the DMOZ directory. The results show that our method achieves a performance improvement for rare categories over the hierarchical classification methods based on full-term and feature-word, and further improves the performance over the whole topic taxonomy.
Starting Page	491
Ending Page	497
Page Count	7
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	5
Issue Number	4
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-10-02
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Text categorization Hierarchical classification Topic taxonomy Latent dirichlet allocation (LDA) Rare category Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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