Convolution Kernels for Natural Language

Abstract

We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discrete structures which require some mechanism to convert them into feature vectors. We describe kernels for various natural language structures, allowing rich, high dimensional representations of these structures. We show how a kernel over trees can be applied to parsing using the voted perceptron algorithm, and we give experimental results on the ATIS corpus of parse trees.

Extracted Key Phrases

Showing 1-10 of 488 extracted citations
050100'02'04'06'08'10'12'14'16
Citations per Year

969 Citations

Semantic Scholar estimates that this publication has received between 820 and 1,144 citations based on the available data.

See our FAQ for additional information.