Abstract
Phonotactic learning is a crucial aspect of phonological acquisition and has figured significantly in computational research in phonology (Prince & Tesar 2004). However, one persistent challenge for this line of research is inducing non-local co-occurrence patterns (Hayes & Wilson 2008). The current study develops a probabilistic phonotactic model based on the Strictly Piecewise class of subregular languages (Heinz 2010). The model successfully learns both segmental and featural representations, and correctly predicts the acceptabilities of the nonce forms in Quechua (Gouskova & Gallagher 2020).
Highlights
Phonotactic learning is a crucial aspect of phonological acquisition and has figured significantly in computational and theoretical research in phonology
The current study has proposed a probabilistic Strictly Piecewise (SP) phonotactic model and a learning algorithm
Through a case study of Quechua laryngeal cooccurence pattern, this paper shows that SP phonotactic model precisely characterizes nonlocal phonotactics and the proposed learner generalizes both segmental and featural representations from noisy corpus data
Summary
Phonotactic learning is a crucial aspect of phonological acquisition and has figured significantly in computational and theoretical research in phonology. One persistent challenge for this line of research is inducing nonlocal cooccurrence patterns (Hayes & Wilson, 2008; Gouskova & Gallagher, 2020). As the length n increases, the search space grows so quickly that it becomes intractable; their learner cannot efficiently detect cooccurrence patterns over arbitrary distances. Instead of directly penalizing the nonlocal dependency of two sibilants, the learner can only inefficiently approximate *s. Most subsequent works on MaxEnt learner generalize nonlocal phonotactics by searching local ngrams over postulated tiers/projections (Wilson & Gallagher, 2018). Gouskova & Gallagher (2020) further offered a method for inducing tiers from placeholder trigrams, their learner is only shown to succeed on data in which the target phonotactics largely occur in local trigrams rather than nonlocal dependency at arbitrary distance Most subsequent works on MaxEnt learner generalize nonlocal phonotactics by searching local ngrams over postulated tiers/projections (Wilson & Gallagher, 2018). Gouskova & Gallagher (2020) further offered a method for inducing tiers from placeholder trigrams, their learner is only shown to succeed on data in which the target phonotactics largely occur in local trigrams rather than nonlocal dependency at arbitrary distance
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