Beyond Semilinearity: Distributional Learning of Parallel Multiple Context-free Grammars

Alexander Clark, Ryo Yoshinaka

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

Semilinearity is widely held to be a linguistic invariant but, controversially, some linguistic phenomena in languages like Old Georgian and Yoruba seem to violate this constraint. In this paper we extend distributional learning to the class of parallel multiple context-free grammars, a class which as far as is known includes all attested natural languages, even taking an extreme view on these examples. These grammars may have a copying operation that can recursively copy constituents, allowing them to generate non-semilinear languages. We generalise the notion of a context to a class of functions that include copying operations. The congruential approach is ineffective at this level of the hierarchy; accordingly we extend this using dual approaches, defining nonterminals using sets of these generalised contexts. As a corollary we also extend the multiple context free grammars using the lattice based approaches.
Original languageEnglish
Title of host publicationProceedings of the Eleventh International Conference on Grammatical Inference
EditorsJeffrey Heinz, Colin de la Higuera, Tim Oates
Pages84-96
Number of pages13
Volume21
Publication statusPublished - 2012

Publication series

NameJMLR Workshop and Conference Proceedings

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