The 12th International Conference on Grammatical Inference, PMLR 34:49-63, 2014.
Abstract
Based on Harris’s substitutability criterion, the recent definitions of classes of substitutable languages have led to interesting polynomial learnability results for expressive formal languages. These classes are also promising for practical applications: in natural language analysis, because definitions have strong linguisitic support, but also in biology for modeling protein families, as suggested in our previous study introducing the class of local substitutable languages. But turning recent theoretical advances into practice badly needs truly practical algorithms. We present here an efficient learning algorithm, motivated by intelligibility and parsing efficiency of the result, which directly reduces the positive sample into a small non-redundant canonical grammar of the target substitutable language. Thanks to this new algorithm, we have been able to extend our experimentation to a complete protein dataset confirming that it is possible to learn grammars on proteins with high specificity and good sensitivity by a generalization based on local substitutability.
@InProceedings{pmlr-v34-coste14a,
title = {A bottom-up efficient algorithm learning substitutable languages from positive examples},
author = {François Coste and Gaëlle Garet and Jacques Nicolas},
booktitle = {The 12th International Conference on Grammatical Inference},
pages = {49--63},
year = {2014},
editor = {Alexander Clark and Makoto Kanazawa and Ryo Yoshinaka},
volume = {34},
series = {Proceedings of Machine Learning Research},
address = {Kyoto, Japan},
month = {17--19 Sep},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v34/coste14a.pdf},
url = {http://proceedings.mlr.press/v34/coste14a.html},
abstract = {Based on Harris’s substitutability criterion, the recent definitions of classes of substitutable languages have led to interesting polynomial learnability results for expressive formal languages. These classes are also promising for practical applications: in natural language analysis, because definitions have strong linguisitic support, but also in biology for modeling protein families, as suggested in our previous study introducing the class of local substitutable languages. But turning recent theoretical advances into practice badly needs truly practical algorithms. We present here an efficient learning algorithm, motivated by intelligibility and parsing efficiency of the result, which directly reduces the positive sample into a small non-redundant canonical grammar of the target substitutable language. Thanks to this new algorithm, we have been able to extend our experimentation to a complete protein dataset confirming that it is possible to learn grammars on proteins with high specificity and good sensitivity by a generalization based on local substitutability.}
}
%0 Conference Paper
%T A bottom-up efficient algorithm learning substitutable languages from positive examples
%A François Coste
%A Gaëlle Garet
%A Jacques Nicolas
%B The 12th International Conference on Grammatical Inference
%C Proceedings of Machine Learning Research
%D 2014
%E Alexander Clark
%E Makoto Kanazawa
%E Ryo Yoshinaka
%F pmlr-v34-coste14a
%I PMLR
%J Proceedings of Machine Learning Research
%P 49--63
%U http://proceedings.mlr.press
%V 34
%W PMLR
%X Based on Harris’s substitutability criterion, the recent definitions of classes of substitutable languages have led to interesting polynomial learnability results for expressive formal languages. These classes are also promising for practical applications: in natural language analysis, because definitions have strong linguisitic support, but also in biology for modeling protein families, as suggested in our previous study introducing the class of local substitutable languages. But turning recent theoretical advances into practice badly needs truly practical algorithms. We present here an efficient learning algorithm, motivated by intelligibility and parsing efficiency of the result, which directly reduces the positive sample into a small non-redundant canonical grammar of the target substitutable language. Thanks to this new algorithm, we have been able to extend our experimentation to a complete protein dataset confirming that it is possible to learn grammars on proteins with high specificity and good sensitivity by a generalization based on local substitutability.
TY - CPAPER
TI - A bottom-up efficient algorithm learning substitutable languages from positive examples
AU - François Coste
AU - Gaëlle Garet
AU - Jacques Nicolas
BT - The 12th International Conference on Grammatical Inference
PY - 2014/08/30
DA - 2014/08/30
ED - Alexander Clark
ED - Makoto Kanazawa
ED - Ryo Yoshinaka
ID - pmlr-v34-coste14a
PB - PMLR
SP - 49
DP - PMLR
EP - 63
L1 - http://proceedings.mlr.press/v34/coste14a.pdf
UR - http://proceedings.mlr.press/v34/coste14a.html
AB - Based on Harris’s substitutability criterion, the recent definitions of classes of substitutable languages have led to interesting polynomial learnability results for expressive formal languages. These classes are also promising for practical applications: in natural language analysis, because definitions have strong linguisitic support, but also in biology for modeling protein families, as suggested in our previous study introducing the class of local substitutable languages. But turning recent theoretical advances into practice badly needs truly practical algorithms. We present here an efficient learning algorithm, motivated by intelligibility and parsing efficiency of the result, which directly reduces the positive sample into a small non-redundant canonical grammar of the target substitutable language. Thanks to this new algorithm, we have been able to extend our experimentation to a complete protein dataset confirming that it is possible to learn grammars on proteins with high specificity and good sensitivity by a generalization based on local substitutability.
ER -
Coste, F., Garet, G. & Nicolas, J.. (2014). A bottom-up efficient algorithm learning substitutable languages from positive examples. The 12th International Conference on Grammatical Inference, in PMLR 34:49-63
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