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Productivity and Predictability for Measuring Morphological Complexity.

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This study introduces a new method to measure language morphological complexity by analyzing word forms and their predictability. Integrating both aspects provides a more comprehensive understanding of a language's structure.

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Area of Science:

  • Computational Linguistics
  • Quantitative Linguistics
  • Natural Language Processing

Background:

  • Existing corpus-based methods for morphological complexity often focus solely on word form productivity.
  • A comprehensive measure should also consider the predictability of morphological processes within words.

Purpose of the Study:

  • To propose a novel quantitative approach for measuring morphological complexity.
  • To integrate both the productivity and predictability of morphological processes into a single framework.
  • To develop a corpus-based method that does not require linguistic annotation.

Main Methods:

  • Utilized a language model to predict sub-word sequences within words.
  • Calculated the entropy rate of the language model as a measure of internal word structure predictability.
  • Applied these measures to two parallel corpora covering a typologically diverse range of languages.

Main Results:

  • Morphological complexity is best understood by integrating both productivity and predictability dimensions.
  • Languages may exhibit high complexity in one aspect (e.g., productivity) but low complexity in another (e.g., predictability).
  • The proposed approach provides a nuanced assessment of morphological complexity across different languages.

Conclusions:

  • The developed quantitative approach offers a more holistic measure of morphological complexity.
  • This method is applicable to typologically diverse languages using readily available parallel corpora.
  • The findings highlight the importance of considering both word formation and internal word structure predictability for linguistic analysis.