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How communicatively optimal are exact numeral systems? Once more on lexicon size and morphosyntactic complexity

Chundra Cathcart, Arne Rubehn, Katja Bocklage, Luca Ciucci, Kellen Parker van Dam, Alžběta Kučerová, Jekaterina Mažara, Carlo Y. Meloni, David Snee, Johann-Mattis List

Abstract

Recent research argues that exact recursive numeral systems optimize communicative efficiency by balancing a tradeoff between the size of the numeral lexicon and the average morphosyntactic complexity (roughly length in morphemes) of numeral terms. We argue that previous studies have not characterized the data in a fashion that accounts for the degree of complexity languages display. Using data from 52 genetically diverse languages and an annotation scheme distinguishing between predictable and unpredictable allomorphy (formal variation), we show that many of the world's languages are decisively less efficient than one would expect. We discuss the implications of our findings for the study of numeral systems and linguistic evolution more generally.

How communicatively optimal are exact numeral systems? Once more on lexicon size and morphosyntactic complexity

Abstract

Recent research argues that exact recursive numeral systems optimize communicative efficiency by balancing a tradeoff between the size of the numeral lexicon and the average morphosyntactic complexity (roughly length in morphemes) of numeral terms. We argue that previous studies have not characterized the data in a fashion that accounts for the degree of complexity languages display. Using data from 52 genetically diverse languages and an annotation scheme distinguishing between predictable and unpredictable allomorphy (formal variation), we show that many of the world's languages are decisively less efficient than one would expect. We discuss the implications of our findings for the study of numeral systems and linguistic evolution more generally.
Paper Structure (10 sections, 1 figure, 1 table)

This paper contains 10 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Materials and methods
  4. Manually segmented and glossed data
  5. Lexicon size and morphosyntactic complexity
  6. Synthetic numeral systems
  7. Mixture modeling
  8. Results
  9. Discussion
  10. Conclusion

Figures (1)

  • Figure 1: Lexicon sizes plotted against morphosyntactic complexity values for languages in our sample, under broad and narrow data formats, with selected languages labeled. Colors represent posterior probabilities of component membership (lighter values indicate higher $P(k=2)$). The solid line represents the Pareto frontier estimated by the evolutionary algorithm.