Abstract

In this paper we investigate category-specific effects through the lens of Welsh mutation. Smith (2011) and Moreton et al. (2017) show that English distinguishes nouns and proper nouns in an experimental blending task. Here we show that Welsh distinguishes nouns, verbs, personal names, and place names in the mutation system. We demonstrate these effects experimentally in a translation task designed to elicit mutation intuitions and in several corpus studies. In addition, we show that these effects correlate with lexical frequency. Deeper statistical analysis and a review of the English data suggests that frequency is a more explanatory factor than part of speech in both languages. We therefore argue that these category-specific effects can be reduced to lexical frequency effects.

Highlights

  • In this paper, we use data from Welsh and English to demonstrate category-specific phonological effects and derive them from frequency effects.Smith (2011) reviews a number of category-specific phonological effects, showing how different parts of speech exhibit differing degrees of faithfulness to the input

  • In this paper we report on a behavioral study using a translation task, designed to elicit Welsh mutation

  • We show how mutation is less likely with less frequent forms and we show how the different parts of speech correlate with lexical frequency as we would expect

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Summary

Introduction

We use data from Welsh and English to demonstrate category-specific phonological effects and derive them from frequency effects. Some Welsh personal names exist as common nouns with distinct meanings Subjects were allowed a lot of latitude in their responses, except for the key parts we were interested in, in the case above, the preposition i and the noun fragdy If they used different words for those elements, they would be prompted for whether they could say the sentence in another way, using the relevant items. The experiment was designed to test various factors all designed to tap into the role of lexical category in mutation: i) lexical category of the triggering element, i.e. prepositions vs adjectives; ii) lexical category of the element undergoing mutation, i.e. common nouns, verbs, and place names; and iii) frequency of place name as targets. Mutation status, is a binary one, the data were analyzed using mixed effects logistic regression (Jaeger 2008). In all of our analyses, we follow the recommendations of Barr et al (2013) using maximal design-based models with random slopes as appropriate.

Lexical category of the trigger
Frequency of place names
Findings
Conclusion
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