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

Alexander Clark, Ryo Yoshinaka
Proceedings of the Eleventh International Conference on Grammatical Inference, PMLR 21:84-96, 2012.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v21-clark12a, title = {Beyond Semilinearity: Distributional Learning of Parallel Multiple Context-free Grammars}, author = {Clark, Alexander and Yoshinaka, Ryo}, booktitle = {Proceedings of the Eleventh International Conference on Grammatical Inference}, pages = {84--96}, year = {2012}, editor = {Heinz, Jeffrey and Higuera, Colin and Oates, Tim}, volume = {21}, series = {Proceedings of Machine Learning Research}, address = {University of Maryland, College Park, MD, USA}, month = {05--08 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v21/clark12a/clark12a.pdf}, url = {https://proceedings.mlr.press/v21/clark12a.html}, 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.} }
Endnote
%0 Conference Paper %T Beyond Semilinearity: Distributional Learning of Parallel Multiple Context-free Grammars %A Alexander Clark %A Ryo Yoshinaka %B Proceedings of the Eleventh International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2012 %E Jeffrey Heinz %E Colin Higuera %E Tim Oates %F pmlr-v21-clark12a %I PMLR %P 84--96 %U https://proceedings.mlr.press/v21/clark12a.html %V 21 %X 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.
RIS
TY - CPAPER TI - Beyond Semilinearity: Distributional Learning of Parallel Multiple Context-free Grammars AU - Alexander Clark AU - Ryo Yoshinaka BT - Proceedings of the Eleventh International Conference on Grammatical Inference DA - 2012/08/16 ED - Jeffrey Heinz ED - Colin Higuera ED - Tim Oates ID - pmlr-v21-clark12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 21 SP - 84 EP - 96 L1 - http://proceedings.mlr.press/v21/clark12a/clark12a.pdf UR - https://proceedings.mlr.press/v21/clark12a.html AB - 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. ER -
APA
Clark, A. & Yoshinaka, R.. (2012). Beyond Semilinearity: Distributional Learning of Parallel Multiple Context-free Grammars. Proceedings of the Eleventh International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 21:84-96 Available from https://proceedings.mlr.press/v21/clark12a.html.

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