Decision trees, entropy, and the contrastive feature hierarchy

Dresher (2009) argues that language-particular hierarchies of features are the best way to identify contrastive features in a phonological inventory. While not universal, this ordering of features is also not fully unconstrained. But what limits the space of possible feature orders remains an open question. This paper demonstrates how the concept of entropy establishes a partial ordering of features that both allows for but also constrains language-particular variation. Specifically, a decision tree machine learning algorithm is employed to dynamically impose structure on the hypothesis space of possible feature orders

Learning Local Phonological Processes

We present a learning algorithm for local phonological processes that relies on a restriction on the expressive power needed to compute phonological patterns that apply locally. Representing phonological processes as a functional mapping from an input to output form (an assumption compatible with either the SPE or OT formalism), the learner assumes the target process can be described with the functional counterpart to the Strictly Local (McNaughton and Papert 1971, Rogers and Pullum 2011) formal languages. Given a data set of input-output string pairs, the learner applies the two-stage grammatical induction procedure of 1) constructing a prefix tree representation of the input and 2) generalizing the pattern to words not found in the data set by merging states (Garcia and Vidal 1990, Oncina et al. 1993, Heinz 2007, 2009, de la Higuera 2010). The learner’s criterion for state merging enforces a locality requirement on the kind of function it can converge to and thereby directly reflects its own hypothesis space. We demonstrate with the example of German final devoicing, using a corpus of string pairs derived from the CELEX2 lemma corpus. The implications of our results include a proposal for how humans generalize to learn phonological patterns and a consequent explanation for why local phonological patterns have this property

The Strict Locality of Phonological Processes

See the PDF

A Computational Account of Tone Sandhi Interaction

This paper presents a computational account of three tone sandhi rules in Tianjin Chinese that have received a lot of attention in the literature due to the seemingly complex way in which they interact. Two of the rules apply right-to-left while the third applies left-to-right, making it difficult for both rule- and constraint-based formalisms to account for the interaction in a unified way. In the computational framework advocated for in this paper, the apparent difference in directionality of the three rules amounts to a subtle difference in computational classification: the left-to-right rule has the property of input strict locality (ISL) while the right-to-left rules share the property of output strict locality (OSL). However, the fact that the direction of rules with the ISL property doesn't actually matter, a unified account becomes possible in that all three rules can be modeled as a single input-output strictly local function

Learning Phonological Mappings by Learning Strictly Local Functions

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 & Papert 1971; Rogers & 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)

How the Structure of the Constraint Space Enables Learning

International audienc

Learning Repairs for Marked Structures

[Abstract not available