Phonological Selection in Small Sublexicons
In Proceedings of Annual Meeting on Phonology 2023-2024, edited by Gerard Avelino, Merlin Balihaxi, Quartz Colvin, Vincent Czarnecki, Hyunjung Joo, Chenli Wang, Utku Zorbarlar, Adam Jardine, Adam McCollum.
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Affixal phonological selection tends to be coarse-grained rather than granular. Stress, syllable count, and C/V composition figure in many examples, while subsegmental/featural generalizations are less common. I argue that this is a consequence of statistical learning. When the learning dataset is comparatively small, only coarse generalizations are reliable. My case study investigates the Russian suffix -ast, which predominantly attaches to body part nouns to form adjectives (e.g., [glaz-ast-ij] ‘big-eyed’). There are only 17 lemmata in common usage, and they respect a size limit (mono- and disyllables). I show that the disyllabic maximum is productive in a corpus study, and that productive use shows frequency matching for syllable count to the learning data but not to nominal stems in general. By contrast, fine-grained featural generalizations about the stem-final consonants appear to be largely ignored in productive use. Speakers extend the suffix to featural contexts unseen in learning data, assuming that the sparse sample is representative–a conclusion supported by the composition of the lexicon. I relate my findings to the Subcategorization Frame theory of selection, the modular separation theory of Scheer (2016), and tolerance theory of Yang (2016).