TY - JOUR
T1 - Efficient learning of multiple context-free languages with multidimensional substitutability from positive data
AU - Yoshinaka, Ryo
N1 - Funding Information:
This work was partially supported by Grant-in-Aid for Young Scientists (B-20700124) and a grant from the Global COE Program, ‘‘Center for Next-Generation Information Technology based on Knowledge Discovery and Knowledge Federation’’, from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
PY - 2011/4/22
Y1 - 2011/4/22
N2 - Recently Clark and Eyraud (2007) [10] have shown that substitutable context-free languages, which capture an aspect of natural language phenomena, are efficiently identifiable in the limit from positive data. Generalizing their work, this paper presents a polynomial-time learning algorithm for new subclasses of multiple context-free languages with variants of substitutability.
AB - Recently Clark and Eyraud (2007) [10] have shown that substitutable context-free languages, which capture an aspect of natural language phenomena, are efficiently identifiable in the limit from positive data. Generalizing their work, this paper presents a polynomial-time learning algorithm for new subclasses of multiple context-free languages with variants of substitutability.
KW - Grammatical inference
KW - Mildly context-sensitive grammars
KW - Multiple context-free grammars
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U2 - 10.1016/j.tcs.2010.12.058
DO - 10.1016/j.tcs.2010.12.058
M3 - Article
AN - SCOPUS:79952630788
SN - 0304-3975
VL - 412
SP - 1821
EP - 1831
JO - Theoretical Computer Science
JF - Theoretical Computer Science
IS - 19
ER -