Name
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 intended structure and is more flexible
and applicable than the learning methods from completely structured examples [5].
|
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/978-3-540-45257-7_19 | ICGI |
Keywords | Field | DocType |
learning context-free grammars,partially structured examples,context free grammar | Rule-based machine translation,Context-free grammar,Formal language,Computer science,Finite-state machine,Grammar,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | ISBN |
1891 | 0302-9743 | 3-540-41011-2 |
Citations | PageRank | References |
27 | 1.23 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yasubumi Sakakibara | 1 | 769 | 62.91 |
| Hidenori Muramatsu | 2 | 27 | 1.23 |