Learning Tier-based Strictly 2-Local Languages | Zendy

Adam Jardine | Zendy; Jeffrey Heinz | Zendy

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Learning Tier-based Strictly 2-Local Languages

Author(s) -

Adam Jardine,

Jeffrey Heinz

Publication year - 2016

Publication title -

transactions of the association for computational linguistics

Language(s) - English

Resource type - Journals

ISSN - 2307-387X

DOI - 10.1162/tacl_a_00085

Subject(s) - computer science , constant (computer programming) , phonotactics , inference , bounded function , class (philosophy) , artificial intelligence , sample (material) , time complexity , theoretical computer science , programming language , natural language processing , algorithm , linguistics , mathematics , phonology , chemistry , chromatography , mathematical analysis , philosophy

The Tier-based Strictly 2-Local (TSL2) languages are a class of formal languages which have been shown to model long-distance phonotactic generalizations in natural language (Heinz et al., 2011). This paper introduces the Tier-based Strictly 2-Local Inference Algorithm (2TSLIA), the first nonenumerative learner for the TSL2 languages. We prove the 2TSLIA is guaranteed to converge in polynomial time on a data sample whose size is bounded by a constant.

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