This paper establishes that unbounded circumambient processes, phonological processes for which crucial information in the environment may appear unboundedly far away on both sides of a target, are common in tonal phonology, but rare in segmental phonology. It then argues that this typological asymmetry is best characterised by positing that tone is more computationally complex than segmental phonology. The evidence for the asymmetry is based around attestations of unbounded tonal plateauing, but it is also shown how the 'sour-grapes' harmony pathology is unbounded circumambient. The paper argues that such processes are not weakly deterministic, which contrasts with previous typological work on segmental phonology. Positing that weak determinism bounds segmental phonology but not tonal phonology thus captures the typological asymmetry. It is also discussed why this explanation is superior to any offered by Optimality Theory.

<p>In this paper we identify strict locality as a defining computational property of the input-output mapping that underlies local phonological processes. We provide an automata-theoretic characterization for the class of Strictly Local functions, which are based on the well-studied Strictly Local formal languages (McNaughton &amp; Papert 1971; Rogers &amp; Pullum 2011; Rogers et al. 2013), and show how they can model a range of phonological processes. We then present a learning algorithm, the SLFLA, which uses the defining property of strict locality as an inductive principle to learn these mappings from finite data. The algorithm is a modification of an algorithm developed by Oncina et al. (1993) (called OSTIA) for learning the class of subsequential functions, of which the SL functions are a proper subset. We provide a proof that the SLFLA learns the class of SL functions and discuss these results alongside previous studies on using OSTIA to learn phonological mappings (Gildea and Jurafsky 1996).</p>

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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.

Autosegmental phonology represents words with graph structures. This paper introduces a way of reasoning about autosegmental graphs as strings of concatenated graph primitives. The main result shows that the sets of autosegmental graphs so generated obey two important, putatively universal, constraints in phonological theory provided that the graph primitives also obey these constraints. These constraints are the Obligatory Contour Principle and the No Crossing Constraint. Thus, these constraints can be understood as being derived from a finite basis under concatenation. This contrasts with (and complements) earlier analyses of autosegmental representations, where these constraints were presented as axioms of the grammatical system. Empirically motivated examples are provided.