Back to Results
First PageMeta Content
Computational statistics / Co-training / Word-sense disambiguation / Active learning / Supervised learning / Natural language processing / Statistical classification / Semi-supervised learning / Machine learning / Artificial intelligence / Learning


In Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing Limitations of Co-Training for Natural Language Learning from Large Datasets David Pierce and Claire Cardie Department of Compute
Add to Reading List

Document Date: 2004-07-16 12:21:47

Open Document

File Size: 145,41 KB

Share Result on Facebook

Company

Penn Treebank / /

Country

Poland / South Korea / /

Currency

USD / /

/

IndustryTerm

web page task factors / given web page / natural language processing task / natural language processing components / web page classification / web page task / modified co-training algorithm / on-line learning / co-training algorithm / natural language processing / straightforward solution / web page classification task / /

Organization

Royal Statistical Society / K. Church / National Park Service / National Science Foundation / US Federal Reserve / Experimental and Theoretical Artificial Intelligence / Warsaw government / Large Datasets David Pierce and Claire Cardie Department of Computer Science Cornell University Ithaca NY / Association for Computational Linguistics / /

Person

C. Cardie / V / Claire Cardie / Mitchell / Blum / /

Position

advisor / Singer / representative / /

ProvinceOrState

Connecticut / /

PublishedMedium

Computational Linguistics / Journal of the Royal Statistical Society / Machine Learning / /

Technology

Natural Language Processing / Knowledge Management / modified co-training algorithm / cotraining algorithm / co-training algorithm / Machine Learning / EM algorithm / CT algorithm / modified CT algorithm / cmp / /

SocialTag