NDLI: Semi-supervised expert metadata extraction based on co-training style

Content Provider	IEEE Xplore Digital Library
Author	Youmin Zhang Zhengtao Yu Li Liu Jianyi Guo Cunli Mao
Copyright Year	2012
Description	Author affiliation: The School of Information Engineering and Automation, Kunming University of Science and Technology, China (Youmin Zhang; Zhengtao Yu; Li Liu; Jianyi Guo; Cunli Mao)
Abstract	Aiming at the problem that requiring large amounts of labeled training data while using supervised learning to extract the expert metadata, a semi-supervised expert metadata extraction method based on co-training style is proposed. Firstly, according to the characteristics of expert metadata, we select expert metadata features and label a certain amount of metadata samples, then train two classifiers with maximum entropy and conditional random respectively. Secondly, two classifiers are used to label metadata items in the unlabeled expert home pages; when the classification results of one type metadata in one expert page satisfy the confidence requirement, analyze the differences of each type metadata labeled by two classifiers; for the metadata satisfying the difference requirement, the better performing classifier for one type metadata is selected to label the certain type metadata, then the labeled expert homepage is obtained as the labeled sample. Finally, use the above-mentioned labeled expert homepage to extend training samples, and retrain two new classifiers, then iterate until two classifiers are convergent. In the experiment, we collected 2000 expert home pages; the results indicate that the semi-supervised expert metadata extraction method based on co-training style outperforms a number of supervised methods, which reduces the amount of manual labeling work effectively.
Starting Page	1344
Ending Page	1347
File Size	258019
Page Count	4
File Format	PDF
ISBN	9781467300254
e-ISBN	9781467300247
DOI	10.1109/FSKD.2012.6234139
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy semi-supervised expert metadata extraction Organizations co-training Learning Feature extraction Classification algorithms Data mining Labeling
Content Type	Text
Resource Type	Article

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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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