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» Name Tagging with Word Clusters and Discriminative Training
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CVPR
2008
IEEE
14 years 8 months ago
Unifying discriminative visual codebook generation with classifier training for object category recognition
The idea of representing images using a bag of visual words is currently popular in object category recognition. Since this representation is typically constructed using unsupervi...
Liu Yang, Rong Jin, Rahul Sukthankar, Fréd&...
NLPRS
2001
Springer
13 years 11 months ago
WordNet and Automated Text Summarization
Proposals for text classification and information retrieval have been recently presented making use of the WordNet ontology. Generally, this methodology requires statistical induc...
Rui Pedro Chaves
SDM
2010
SIAM
259views Data Mining» more  SDM 2010»
13 years 8 months ago
Semi-supervised Bio-named Entity Recognition with Word-Codebook Learning
We describe a novel semi-supervised method called WordCodebook Learning (WCL), and apply it to the task of bionamed entity recognition (bioNER). Typical bioNER systems can be seen...
Pavel P. Kuksa, Yanjun Qi
ACL
2009
13 years 4 months ago
Phrase Clustering for Discriminative Learning
We present a simple and scalable algorithm for clustering tens of millions of phrases and use the resulting clusters as features in discriminative classifiers. To demonstrate the ...
Dekang Lin, Xiaoyun Wu
ACL
2008
13 years 8 months ago
Semi-Supervised Sequential Labeling and Segmentation Using Giga-Word Scale Unlabeled Data
This paper provides evidence that the use of more unlabeled data in semi-supervised learning can improve the performance of Natural Language Processing (NLP) tasks, such as part-o...
Jun Suzuki, Hideki Isozaki