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Article published In: Explorations of morphological structure in distributional space
Edited by Melanie J. Bell, Juhani Järvikivi and Vito Pirrelli
[The Mental Lexicon 17:3] 2022
► pp. 337–367

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Making sense of spoken plurals

ORCiD logo with link | Eberhard Karls Universität Tübingen
 | Friedrich-Alexander-Universität Erlangen-Nürnberg
 | Eberhard Karls Universität Tübingen
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Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 17 August 2023
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https://doi.org/10.1075/ml.22011.sha
Abstract
Distributional semantics offers new ways to study the semantics of morphology. This study focuses on the semantics of noun singulars and their plural inflectional variants in English. Our goal is to compare two models for the conceptualization of plurality. One model (FRACSS) proposes that all singular-plural pairs should be taken into account when predicting plural semantics from singular semantics. The other model (CCA) argues that conceptualization for plurality depends primarily on the semantic class of the base word. We compare the two models on the basis of how well the speech signal of plural tokens in a large corpus of spoken American English aligns with the semantic vectors predicted by the two models. Two measures are employed: the performance of a form-to-meaning mapping and the correlations between form distances and meaning distances. Results converge on a superior alignment for CCA. Our results suggest that usage-based approaches to pluralization in which a given word’s own semantic neighborhood is given priority outperform theories according to which pluralization is conceptualized as a process building on high-level abstraction. We see that what has often been conceived of as a highly abstract concept, [+plural], is better captured via a family of mid-level partial generalizations.
Article outline
  • 1.Introduction
  • 2.Data
  • 3.Study 1: Assessing the isomorphy of form and meaning with LDL-AURIS
    • 3.1Representations
    • 3.2Mapping audio vectors onto semantic vectors
    • 3.3Training data and test data
    • 3.4Procedure
    • 3.5Results
      • 3.5.1Recognitions
      • 3.5.2Errors
    • 3.6An alternative approach
  • 4.Study 2: Assessing the isomorphy of form and meaning with distance measures
  • 5.General Discussion
  • Acknowledgements
  • Authors’ contributions
  • Notes
  • References
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