What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity
This work addresses the validation of complexity measures for linguists and typologists, but it is incremental as it focuses on correlating existing measures rather than introducing new ones.
The authors analyzed eight corpus-based measures of morphological complexity in natural languages, finding that the first principal component explains 92.62% of the variation, indicating strong linear dependence among these measures.
We present an analysis of eight measures used for quantifying morphological complexity of natural languages. The measures we study are corpus-based measures of morphological complexity with varying requirements for corpus annotation. We present similarities and differences between these measures visually and through correlation analyses, as well as their relation to the relevant typological variables. Our analysis focuses on whether these `measures' are measures of the same underlying variable, or whether they measure more than one dimension of morphological complexity. The principal component analysis indicates that the first principal component explains 92.62 % of the variation in eight measures, indicating a strong linear dependence between the complexity measures studied.