NDLI: Bridging Domains Using World Wide Knowledge for Transfer Learning

Content Provider	IEEE Xplore Digital Library
Author	Xiang, E.W. Bin Cao Hu, D.H. Qiang Yang
Copyright Year	1989
Abstract	A major problem of classification learning is the lack of ground-truth labeled data. It is usually expensive to label new data instances for training a model. To solve this problem, domain adaptation in transfer learning has been proposed to classify target domain data by using some other source domain data, even when the data may have different distributions. However, domain adaptation may not work well when the differences between the source and target domains are large. In this paper, we design a novel transfer learning approach, called BIG (Bridging Information Gap), to effectively extract useful knowledge in a worldwide knowledge base, which is then used to link the source and target domains for improving the classification performance. BIG works when the source and target domains share the same feature space but different underlying data distributions. Using the auxiliary source data, we can extract a ¿bridge¿ that allows cross-domain text classification problems to be solved using standard semisupervised learning algorithms. A major contribution of our work is that with BIG, a large amount of worldwide knowledge can be easily adapted and used for learning in the target domain. We conduct experiments on several real-world cross-domain text classification tasks and demonstrate that our proposed approach can outperform several existing domain adaptation approaches significantly.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	770
Ending Page	783
Page Count	14
File Size	5364595
File Format	PDF
ISSN	10414347
Volume Number	22
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Text categorization Wikipedia Information retrieval Semisupervised learning Content based retrieval Web search Supervised learning Terminology Knowledge transfer Wikipedia. transfer learning cross-domain text classification
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Noise free information retrieval using web content mining on web pages

Enabling concept-based relevance feedback for information retrieval on the WWW

Tri-training: exploiting unlabeled data using three classifiers

Personalized Content Retrieval in Context Using Ontological Knowledge

Label Propagation through Linear Neighborhoods

A Relation-Based Page Rank Algorithm for Semantic Web Search Engines

Web People Search via Connection Analysis

Text classification without negative examples revisit

Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective

Bridging Domains Using World Wide Knowledge for Transfer Learning

Similar Documents

Noise free information retrieval using web content mining on web pages

Enabling concept-based relevance feedback for information retrieval on the WWW

Tri-training: exploiting unlabeled data using three classifiers

Personalized Content Retrieval in Context Using Ontological Knowledge

Label Propagation through Linear Neighborhoods

A Relation-Based Page Rank Algorithm for Semantic Web Search Engines

Web People Search via Connection Analysis

Text classification without negative examples revisit

Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective

Bridging Domains Using World Wide Knowledge for Transfer Learning