Idiosyncratic frequency as a measure of derivation vs. inflection

Published in Journal of Language Modelling, 2022

Recommended citation: Maria Copot, Timothee Mickus and Olivier Bonami (accepted). Idiosyncratic frequency as a measure of derivation vs. inflection. In Journal of Language Modelling.

There is ongoing discussion about how to conceptualize the nature of the distinction between inflection and derivation. A common approach relies on qualitative differences in the semantic relationship between inflectionally versus derivationally related words: inflection yields ways to discuss the same concept in different syntactic contexts, while derivation gives rise to words for related concepts. This differential can be expected to manifest in the predictability of word frequency between words that are related derivationally or inflectionally: predicting the token frequency of a word based on information about its base form or about related words should be easier when the two words are in an inflectional relationship, rather than a derivational one. We compare prediction error magnitude for statistical models of token frequency based on distributional and frequency information of inflectionally or derivationally related words in French. The results conform to expectations: it is easier to predict the frequency of a word from properties of an inflectionally-related word than from those of a derivationally-related word. Prediction error provides a quantitative, continuous method to explore differences between individual processes and differences yielded by employing different predicting information, which in turn can be used to draw conclusions about the nature and manifestation of the inflection–derivation distinction.


Recommended citation: Copot et al. (accepted). “Idiosyncratic frequency as a measure of derivation vs. inflection”. In Journal of Language Modelling.