Towards General Algorithms for Grammatical Inference
Abstract
Many algorithms for grammatical inference can be viewed as instances of a more general algorithm which maintains a set of primitive elements, which distributionally define sets of strings, and a set of features or tests that constrain various inference rules. Using this general framework, which we cast as a process of logical inference, we re-analyse Angluin’s famous lstar algorithm and several recent algorithms for the inference of context-free grammars and multiple context-free grammars. Finally, to illustrate the advantages of this approach, we extend it to the inference of functional transductions from positive data only, and we present a new algorithm for the inference of finite state transducers.
Cite
Text
Clark. "Towards General Algorithms for Grammatical Inference." International Conference on Algorithmic Learning Theory, 2010. doi:10.1007/978-3-642-16108-7_2Markdown
[Clark. "Towards General Algorithms for Grammatical Inference." International Conference on Algorithmic Learning Theory, 2010.](https://mlanthology.org/alt/2010/clark2010alt-general/) doi:10.1007/978-3-642-16108-7_2BibTeX
@inproceedings{clark2010alt-general,
title = {{Towards General Algorithms for Grammatical Inference}},
author = {Clark, Alexander},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {2010},
pages = {11-30},
doi = {10.1007/978-3-642-16108-7_2},
url = {https://mlanthology.org/alt/2010/clark2010alt-general/}
}