Languages as Hyperplanes: grammatical inference with string kernels

Alexander Clark; Christophe Costa Florêncio; Chris Watkins

Languages as Hyperplanes: grammatical inference with string kernels

Alexander Clark, Christophe Costa Florêncio, Chris Watkins

Philosophy

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

12 Citations (Scopus)

Abstract

Using string kernels, languages can be represented as hyperplanes in a high dimensional feature space. We present a new family of grammatical inference algorithms based on this idea. We demonstrate that some mildly context sensitive languages can be represented in this way and it is possible to efficiently learn these using kernel PCA. We present some experiments demonstrating the effectiveness of this approach on some standard examples of context sensitive languages using small synthetic data sets.

Original language	Undefined/Unknown
Title of host publication	Proceedings of the European Conference on Machine Learning (ECML)
Pages	90-101
Number of pages	12
Volume	4212 LNAI
Publication status	Published - 2006

Cite this

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title = "Languages as Hyperplanes: grammatical inference with string kernels",

abstract = "Using string kernels, languages can be represented as hyperplanes in a high dimensional feature space. We present a new family of grammatical inference algorithms based on this idea. We demonstrate that some mildly context sensitive languages can be represented in this way and it is possible to efficiently learn these using kernel PCA. We present some experiments demonstrating the effectiveness of this approach on some standard examples of context sensitive languages using small synthetic data sets.",

author = "Alexander Clark and {Costa Flor{\^e}ncio}, Christophe and Chris Watkins",

year = "2006",

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AU - Clark, Alexander

AU - Costa Florêncio, Christophe

AU - Watkins, Chris

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AB - Using string kernels, languages can be represented as hyperplanes in a high dimensional feature space. We present a new family of grammatical inference algorithms based on this idea. We demonstrate that some mildly context sensitive languages can be represented in this way and it is possible to efficiently learn these using kernel PCA. We present some experiments demonstrating the effectiveness of this approach on some standard examples of context sensitive languages using small synthetic data sets.

M3 - Conference paper

VL - 4212 LNAI

SP - 90

EP - 101

BT - Proceedings of the European Conference on Machine Learning (ECML)

ER -