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].
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Sakakibara, Y., Muramatsu, H. (2000). Learning Context-Free Grammars from Partially Structured Examples. In: Oliveira, A.L. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2000. Lecture Notes in Computer Science(), vol 1891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45257-7_19
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DOI: https://doi.org/10.1007/978-3-540-45257-7_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41011-9
Online ISBN: 978-3-540-45257-7
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