Word-length entropies and correlations of natural language written texts
This work provides incremental insights into linguistic patterns for computational linguistics and natural language processing researchers.
The study analyzed word-length distributions and correlations across ten European languages, finding that short-word metrics distinguish Finnish, long-word tails differentiate Germanic from Romanic languages and Greek, and word-length correlations are weaker in Germanic and Finnish languages.
We study the frequency distributions and correlations of the word lengths of ten European languages. Our findings indicate that a) the word-length distribution of short words quantified by the mean value and the entropy distinguishes the Uralic (Finnish) corpus from the others, b) the tails at long words, manifested in the high-order moments of the distributions, differentiate the Germanic languages (except for English) from the Romanic languages and Greek and c) the correlations between nearby word lengths measured by the comparison of the real entropies with those of the shuffled texts are found to be smaller in the case of Germanic and Finnish languages.