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AAAI
1998
14 years 11 months ago
Learning to Classify Text from Labeled and Unlabeled Documents
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper sh...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
ASSETS
2008
ACM
14 years 11 months ago
Hunting for headings: sighted labeling vs. automatic classification of headings
Proper use of headings in web pages can make navigation more efficient for blind web users by indicating semantic di visions in the page. Unfortunately, many web pages do not use ...
Jeremy T. Brudvik, Jeffrey P. Bigham, Anna Cavende...
FCSC
2010
238views more  FCSC 2010»
14 years 7 months ago
Knowledge discovery through directed probabilistic topic models: a survey
Graphical models have become the basic framework for topic based probabilistic modeling. Especially models with latent variables have proved to be effective in capturing hidden str...
Ali Daud, Juanzi Li, Lizhu Zhou, Faqir Muhammad
ACL
2006
14 years 11 months ago
A FrameNet-Based Semantic Role Labeler for Swedish
We present a FrameNet-based semantic role labeling system for Swedish text. As training data for the system, we used an annotated corpus that we produced by transferring FrameNet ...
Richard Johansson, Pierre Nugues
EMNLP
2006
14 years 11 months ago
Learning Field Compatibilities to Extract Database Records from Unstructured Text
Named-entity recognition systems extract entities such as people, organizations, and locations from unstructured text. Rather than extract these mentions in isolation, this paper ...
Michael L. Wick, Aron Culotta, Andrew McCallum