Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410603 | Neurocomputing | 2009 | 7 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Nicola Cancedda, Pierre Mahé,