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.
Cite
Text
Clark et al. "Languages as Hyperplanes: Grammatical Inference with String Kernels." European Conference on Machine Learning, 2006. doi:10.1007/11871842_13Markdown
[Clark et al. "Languages as Hyperplanes: Grammatical Inference with String Kernels." European Conference on Machine Learning, 2006.](https://mlanthology.org/ecmlpkdd/2006/clark2006ecml-languages/) doi:10.1007/11871842_13BibTeX
@inproceedings{clark2006ecml-languages,
title = {{Languages as Hyperplanes: Grammatical Inference with String Kernels}},
author = {Clark, Alexander and Florêncio, Christophe Costa and Watkins, Chris},
booktitle = {European Conference on Machine Learning},
year = {2006},
pages = {90-101},
doi = {10.1007/11871842_13},
url = {https://mlanthology.org/ecmlpkdd/2006/clark2006ecml-languages/}
}