Efficient Learning of Tier-Based Strictly k-Local Languages

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10168)


We introduce an algorithm that learns the class of Tier-based Strictly k-Local (TSL\(_k\)) formal languages in polynomial time on a sample of positive data whose size is bounded by a constant. The TSL\(_k\) languages are useful in modeling the cognition of sound patterns in natural language [6, 11], and it is known that they can be efficiently learned from positive data in the case that \(k=2\) [9]. We extend this result to any k and improve on its time efficiency. We also refine the definition of a canonical TSL\(_k\) grammar and prove several properties about these grammars that aid in their learning.


Grammatical inference Algorithmic learning 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of LinguisticsRutgers UniversityNew BrunswickUSA
  2. 2.Department of LinguisticsUniversity of OttawaOttawaCanada

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