TY - GEN

T1 - Learning context-free grammars from partially structured examples

AU - Sakakibara, Yasubumi

AU - Muramatsu, Hidenori

N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.

PY - 2000

Y1 - 2000

N2 - In this paper, we consider the problem of inductively learning context-free grammars from partially structured examples. A structured example is represented by a string with some parentheses inserted to indicate the shape of the derivation tree of a grammar. We show that the partially structured examples contribute to improving the efficiency of the learning algorithm. We employ the GA-based learning algorithm for context-free grammars using tabular representations which Sakakibara and Kondo have proposed previously [7], and present an algorithm to eliminate unnecessary nonterminals and production rules using the partially structured examples at the initial stage of the GA-based learning algorithm. We also show that our learning algorithm from partially structured examples can identify a context-free grammar having the in- tended structure and is more flexible and applicable than the learning methods from completely structured examples [5].

AB - In this paper, we consider the problem of inductively learning context-free grammars from partially structured examples. A structured example is represented by a string with some parentheses inserted to indicate the shape of the derivation tree of a grammar. We show that the partially structured examples contribute to improving the efficiency of the learning algorithm. We employ the GA-based learning algorithm for context-free grammars using tabular representations which Sakakibara and Kondo have proposed previously [7], and present an algorithm to eliminate unnecessary nonterminals and production rules using the partially structured examples at the initial stage of the GA-based learning algorithm. We also show that our learning algorithm from partially structured examples can identify a context-free grammar having the in- tended structure and is more flexible and applicable than the learning methods from completely structured examples [5].

UR - http://www.scopus.com/inward/record.url?scp=84974705310&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84974705310&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-45257-7_19

DO - 10.1007/978-3-540-45257-7_19

M3 - Conference contribution

AN - SCOPUS:84974705310

SN - 9783540452577

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 229

EP - 240

BT - Grammatical Inference

A2 - Oliveira, Arlindo L.

PB - Springer Verlag

T2 - 5th International Colloquium on Grammatical Inference, ICGI 2000

Y2 - 11 September 2000 through 13 September 2000

ER -