Abstract
Several algorithms have been proposed to learn different subclasses of context-free grammars based on the idea generically called distributional learning. Those techniques have been applied to many formalisms richer than context-free grammars like multiple context-free grammars, simple contextfree tree grammars and others. The learning algorithms for those different formalisms are actually quite similar to each other. We in this paper give a uniform view on those algorithms.
Highlights
Approaches based on the idea generically called distributional learning have been making great success in the algorithmic learning of various subclasses of context-free grammars (CFGs) (Clark, 2010c; Yoshinaka, 2012)
Every grammar formalism for which distributional learning techniques have been proposed so far generate their languages through context-free derivation trees, whose nodes are labeled by production rules
We show that grammars with different distributional properties are learnable by standard distributional learning techniques if the formalism satisfies some conditions, which include polynomialtime decomposability of objects into contexts and Proceedings of the 14th Meeting on the Mathematics of Language (MoL 14), pages 87–98, Chicago, USA, July 25–26, 2015. c 2015 Association for Computational Linguistics substructures
Summary
Approaches based on the idea generically called distributional learning have been making great success in the algorithmic learning of various subclasses of context-free grammars (CFGs) (Clark, 2010c; Yoshinaka, 2012). Those techniques are applied to richer formalisms as well. In the CFG case, c is a string pair ⟨l, r⟩ and s is a string u and ⟨l, r⟩ ⊙ u = lur, which may correspond to a derivation I ⇒∗ lXr ⇒∗ lur where I is the initial symbol and X is a nonterminal symbol In richer formalisms those substructures and contexts may have richer structures, like tuples of strings or λ-terms. We discuss cases where we cannot enumerate all of the possible contexts and substructures
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