Back to Results
First PageMeta Content
Artificial intelligence / Science / Machine learning / Biology / Semi-supervised learning / Domain / Co-training / Information extraction / Natural language processing / Protein domains / Protein structure


Intra-document Structural Frequency Features for Semi-supervised Domain Adaptation Andrew Arnold William W. Cohen
Add to Reading List

Document Date: 2008-09-18 15:17:58

Open Document

File Size: 488,96 KB

Share Result on Facebook

City

San Francisco / New York / Sydney / Morristown / Cambridge / Nice / /

Company

MIT Press / Medline / /

Country

France / United States / Australia / /

Currency

USD / /

/

Event

Product Recall / Product Issues / /

Facility

Carnegie Mellon University / We mine / Domain Adaptation Andrew Arnold William W. Cohen Machine Learning Department Carnegie Mellon University / USA Machine Learning Department Carnegie Mellon University / University of Wisconsin / /

IndustryTerm

end-user applications / structured search engine / on-line archive / km text mining / information extraction systems / protein name search engines / learning algorithm / /

Organization

MIT / Carnegie Mellon University / University of Wisconsin / /

Person

FULL FULL FULL / William W. Cohen / Morgan Kaufmann / Andrew Arnold William / /

Position

extractor / researcher / existing extractor / Model / Knowledge modeling General / evaluator / special evaluator / domain specialist / /

Product

Minorthird / Precision vs / COLT / curve / 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 / extractors / /

ProvinceOrState

Wisconsin / New York / California / Massachusetts / /

PublishedMedium

Computational Linguistics / Machine Learning / Journal of Artificial Intelligence Research / /

TVStation

KSCE / /

Technology

Bioinformatics / search engine / Machine Learning / PDF / learning algorithm / Knowledge Management / e-PDF / Data Mining / /

URL

http /

SocialTag