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2017 | OriginalPaper | Buchkapitel

The Strong, Weak, and Very Weak Finite Context and Kernel Properties

verfasst von : Makoto Kanazawa, Ryo Yoshinaka

Erschienen in: Language and Automata Theory and Applications

Verlag: Springer International Publishing

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Abstract

We identify the properties of context-free grammars that exactly correspond to the behavior of the dual and primal versions of Clark and Yoshinaka’s distributional learning algorithm and call them the very weak finite context/kernel property. We show that the very weak finite context property does not imply Yoshinaka’s weak finite context property, which has been assumed to hold of the target language for the dual algorithm to succeed. We also show that the weak finite context property is genuinely weaker than Clark’s strong finite context property, settling a question raised by Yoshinaka.

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Fußnoten
1
Clark and Yoshinaka have used the term “distributional learning” more loosely in connection with a number of different learning paradims (see [5] for a survey).
 
2
The present formulations follow [4].
 
3
It is clear from Ogden’s proof that the lemma is really about one particular derivation tree of a context-free grammar. If p is the constant of Ogden’s lemma for G, we obtain the required decomposition of the derivation tree by first marking the initial \(a^p\), then the \(b^p\) preceding \(\#\), and then the \(a^p\) immediately following \(\#\).
 
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Metadaten
Titel
The Strong, Weak, and Very Weak Finite Context and Kernel Properties
verfasst von
Makoto Kanazawa
Ryo Yoshinaka
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-53733-7_5