Languages with longer words have more lexical change

The findings to be presented in this paper were not anticipated, but came about as an unexpected result of looking at how the application of a version of the Levenshtein distance to word lists compares with cognate counting. We were interested in the degree to which the two correlate. The results of this investigation are intrinsically interesting and will be presented in the following section 2, but even more interesting is our finding that differences between counting cognates and measuring the Levenshtein distances vary as a function of average word lengths in the word lists compared. This observation will occupy the remainder of the paper, with section 3 devoted to establishing the sta tis tical significance of the observation across language families, while section 4 establishes the significance within language groups, and section 5 discusses competing explanations. First we briefly explain the specific version of the Levenshtein distance used and the concept of cognate identification. In numerous previous papers, beginning in Holman et al. (2008a), the present authors as well as other members of the network of scholars participating in the project known as ASJP (or Automated Similarity Judgment Pro gram) have applied a computer-assisted comparison of word lists in order to derive a measure of differences among languages. Our method consists in comparing pairs of words to determine the Levenshtein distance, LD, which is defined as the number of substitutions, insertions, and deletions necessary to transform one word into another. The LD is divided by the length of the longer of the two words compared such that any distance will come to lie in the range 0%–100%. This normalized measure, called LDN,2 is averaged over all pairs of words referring to the same concept in lists from two given languages. To enhance discrimination between related and unrelated languages, this average LDN is further divided by the average LDN between words referring to dif ferent concepts in the different lists, to obtain what we call LDND (‘Leven shtein Distance Normalized Divided’). A similarity measure, here called ASJPsim, is defined by subtracting LDND from 100%.

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