Paper Info
Title
On Reverse Feature Engineering of Syntactic Tree Kernels.
Abstract
In this paper, we provide a theoretical framework for feature selection in tree kernel spaces based on gradient-vector components of kernel-based machines. We show that a huge number of features can be discarded without a significant decrease in accuracy. Our selection algorithm is as accurate as and much more efficient than those proposed in previous work. Comparative experiments on three interesting and very diverse classification tasks, i.e. Question Classification, Relation Extraction and Semantic Role Labeling, support our theoretical findings and demonstrate the algorithm performance.
Year
Venue
Keywords
2010
CoNLL
algorithm performance,diverse classification task,theoretical framework,gradient-vector component,theoretical finding,relation extraction,comparative experiment,feature selection,selection algorithm,syntactic tree kernel,semantic role,reverse feature engineering
Field
DocType
Citations 
Kernel (linear algebra),Pattern recognition,Feature selection,Computer science,Selection algorithm,Tree kernel,Feature engineering,Artificial intelligence,Syntax,Semantic role labeling,Machine learning,Relationship extraction
Conference
11
PageRank 
References 
Authors
0.55
33
2
Name
Order
Citations
PageRank
daniele pighin128918.72
Alessandro Moschitti23262177.68