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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program


Year Degree Awarded


Month Degree Awarded


First Advisor

Michael Becker

Second Advisor

Gaja Jarosz

Subject Categories

Computational Linguistics | Phonetics and Phonology


This dissertation proposes the Restrictive Tier Learner, which automatically induces only the tiers that are absolutely necessary in capturing phonological long-distance dependencies. The core of my learner is the addition of an extra evaluation step to the existing Inductive Projection Learner (Gouskova and Gallagher 2020), where the necessity and accuracy of the candidate tiers are determined.

An important building block of my learner is a typological observation, namely the dichotomy between trigram-bound and unbounded patterns. The fact that this dichotomy is attested in both consonant interactions and vowel interactions allows for a unified approach to be used. Another important piece of information is that only unboundedness implies trigram-boundedness, and not vice versa. These typological observations together shed light on the critical role of trigrams in phonological learning. The premise that there is no other distance at which a restriction holds than these two lets us safely assume that searching only up to trigrams might actually be a near-exhaustive search for local interactions. On top of that, the fact that interaction beyond a trigram window, which we need tiers for, always implies interaction within a trigram window guarantees that all necessary tiers can be discovered by looking at trigram constraints. Hence, a learner can confidently search up to trigrams for local interactions and expand its search for non-local ones from the discovered trigrams.

I present several case studies to test the abilities of the Restrictive Tier Learner in capturing various long-distance dependencies that are attested in natural languages. The current version of the learner maintains all the strengths of the previous learning algorithms while showing improved performance in critical cases.