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» Learning to classify short and sparse text
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ICML
2006
IEEE
14 years 5 months ago
Practical solutions to the problem of diagonal dominance in kernel document clustering
In supervised kernel methods, it has been observed that the performance of the SVM classifier is poor in cases where the diagonal entries of the Gram matrix are large relative to ...
Derek Greene, Padraig Cunningham
ICDM
2005
IEEE
217views Data Mining» more  ICDM 2005»
13 years 10 months ago
Improving Automatic Query Classification via Semi-Supervised Learning
Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose web search systems. Such classification becomes critical if th...
Steven M. Beitzel, Eric C. Jensen, Ophir Frieder, ...
BMCBI
2005
160views more  BMCBI 2005»
13 years 4 months ago
Data-poor categorization and passage retrieval for Gene Ontology Annotation in Swiss-Prot
Background: In the context of the BioCreative competition, where training data were very sparse, we investigated two complementary tasks: 1) given a Swiss-Prot triplet, containing...
Frédéric Ehrler, Antoine Geissbü...
LREC
2010
174views Education» more  LREC 2010»
13 years 6 months ago
SINotas: the Evaluation of a NLG Application
SINotas is a data-to-text NLG application intended to produce short textual reports on students'academic performance from a database conveying their grades, weekly attendance...
Roberto P. A. Araujo, Rafael L. de Oliveira, Eder ...
EMNLP
2006
13 years 6 months ago
Boosting Unsupervised Relation Extraction by Using NER
Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the ...
Ronen Feldman, Benjamin Rosenfeld