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ACL
2004
13 years 6 months ago
Convolution Kernels with Feature Selection for Natural Language Processing Tasks
Convolution kernels, such as sequence and tree kernels, are advantageous for both the concept and accuracy of many natural language processing (NLP) tasks. Experiments have, howev...
Jun Suzuki, Hideki Isozaki, Eisaku Maeda
ICDM
2005
IEEE
143views Data Mining» more  ICDM 2005»
13 years 11 months ago
Effective Estimation of Posterior Probabilities: Explaining the Accuracy of Randomized Decision Tree Approaches
There has been increasing number of independently proposed randomization methods in different stages of decision tree construction to build multiple trees. Randomized decision tre...
Wei Fan, Ed Greengrass, Joe McCloskey, Philip S. Y...
NAACL
2007
13 years 6 months ago
Tree Revision Learning for Dependency Parsing
We present a revision learning model for improving the accuracy of a dependency parser. The revision stage corrects the output of the base parser by means of revision rules learne...
Giuseppe Attardi, Massimiliano Ciaramita
CP
2009
Springer
14 years 6 months ago
Minimising Decision Tree Size as Combinatorial Optimisation
Decision tree induction techniques attempt to find small trees that fit a training set of data. This preference for smaller trees, which provides a learning bias, is often justifie...
Christian Bessiere, Emmanuel Hebrard, Barry O'Sull...
COLING
2002
13 years 5 months ago
A Stochastic Parser Based on an SLM with Arboreal Context Trees
In this paper, we present a parser based on a stochastic structured language model (SLM) with a exible history reference mechanism. An SLM is an alternative to an n-gram model as...
Shinsuke Mori