The exponential distribution of the order of demonstrative, numeral, adjective and noun

TL;DR

This paper demonstrates that the distribution of preferred word orders in noun phrases is better modeled by an exponential distribution than a power law, challenging common assumptions about linguistic frequency patterns.

Contribution

It provides evidence that exponential models better fit word order distributions, suggesting no hard constraints on word order variation and undersampling explains unattested orders.

Findings

Exponential distribution fits word order data better than power law.

All 24 possible orders have non-zero probability under the exponential model.

Undersampling accounts for unattested word orders.

Abstract

The frequency of the preferred order for a noun phrase formed by demonstrative, numeral, adjective and noun has received significant attention over the last two decades. We investigate the actual distribution of the 24 possible orders. There is no consensus on whether it is well-fitted by an exponential or a power law distribution. We find that an exponential distribution is a much better model. This finding and other circumstances where an exponential-like distribution is found challenge the view that power-law distributions, e.g., Zipf's law for word frequencies, are inevitable. We also investigate which of two exponential distributions gives a better fit: an exponential model where the 24 orders have non-zero probability (a geometric distribution truncated at rank 24) or an exponential model where the number of orders that can have non-zero probability is variable (a right-truncated geometric distribution). When consistency and generalizability are prioritized, we find higher support for the exponential model where all 24 orders have non-zero probability. These findings strongly suggest that there is no hard constraint on word order variation and then unattested orders merely result from undersampling, consistently with Cysouw's view.