### Abstract

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].

Original language | English |
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Title of host publication | Grammatical Inference: Algorithms and Applications - 5th International Colloquium, ICGI 2000, Proceedings |

Publisher | Springer Verlag |

Pages | 229-240 |

Number of pages | 12 |

Volume | 1891 |

ISBN (Print) | 9783540452577 |

Publication status | Published - 2000 |

Externally published | Yes |

Event | 5th International Colloquium on Grammatical Inference, ICGI 2000 - Lisbon, Portugal Duration: 2000 Sep 11 → 2000 Sep 13 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 1891 |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 5th International Colloquium on Grammatical Inference, ICGI 2000 |
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Country | Portugal |

City | Lisbon |

Period | 00/9/11 → 00/9/13 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Grammatical Inference: Algorithms and Applications - 5th International Colloquium, ICGI 2000, Proceedings*(Vol. 1891, pp. 229-240). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1891). Springer Verlag.

**Learning context-free grammars from partially structured examples.** / Sakakibara, Yasubumi; Muramatsu, Hidenori.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Grammatical Inference: Algorithms and Applications - 5th International Colloquium, ICGI 2000, Proceedings.*vol. 1891, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1891, Springer Verlag, pp. 229-240, 5th International Colloquium on Grammatical Inference, ICGI 2000, Lisbon, Portugal, 00/9/11.

}

TY - GEN

T1 - Learning context-free grammars from partially structured examples

AU - Sakakibara, Yasubumi

AU - Muramatsu, Hidenori

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

M3 - Conference contribution

AN - SCOPUS:84974705310

SN - 9783540452577

VL - 1891

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: Algorithms and Applications - 5th International Colloquium, ICGI 2000, Proceedings

PB - Springer Verlag

ER -