PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximate Kernels for Trees
Konrad Rieck, Tammo Krueger and Ulf Brefeld
FIRST Reports 5/2008, Fraunhofer Institute FIRST 2008. ISSN 1613-5024


Convolution kernels for trees provide effective means for learning with tree-structured data, such as parse trees of natural language sentences. Unfortunately, the computation time of tree kernels is quadratic in the size of the trees as all pairs of nodes need to be compared: large trees render convolution kernels inapplicable. In this paper, we propose a simple but efficient approximation technique for tree kernels. The approximate tree kernel (ATK) accelerates computation by selecting a sparse and discriminative subset of subtrees using a linear program. The kernel allows for incorporating domain knowledge and controlling the overall computation time through additional constraints. Experiments on applications of natural language processing and web spam detection demonstrate the efficiency of the approximate kernels. We observe run-time improvements of two orders of magnitude while preserving the discriminative expressiveness and classification rates of regular convolution kernels.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:4173
Deposited By:Konrad Rieck
Deposited On:13 October 2008