PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Factored Sequence Kernels
Nicola Cancedda and Pierre Mahé
Neurocomputing Volume 72, Number 7-9, pp. 1407-1413, 2009.

Abstract

In this paper we propose an extension of sequence kernels to the case where the symbols that define the sequences have multiple representations. This configuration occurs, for instance, in natural language processing, where words can be characterized according to different linguistic dimensions. The core of our contribution is to integrate early the different representations in the kernel, in a way that generates rich composite features defined across the various symbol dimensions.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
ID Code:4844
Deposited By:Nicola Cancedda
Deposited On:24 March 2009