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This paper describes a computerized alternative to glottochronology for estimating elapsed time since parent languages diverged into daughter languages. The method, developed by the Automated Similarity Judgment Program (ASJP) consortium, is different from glottochronology in four major respects: (1) it is automated and thus is more objective, (2) it applies a uniform analytical approach to a single database of worldwide languages, (3) it is based on lexical similarity as determined from Levenshtein (edit) distances rather than on cognate percentages, and (4) it provides a formula for date calculation that mathematically recognizes the lexical heterogeneity of individual languages, including parent languages just before their breakup into daughter languages. Automated judgments of lexical similarity for groups of related languages are calibrated with historical, epigraphic, and archaeological divergence dates for 52 language groups. The discrepancies between estimated and calibration dates are found to be on average 29% as large as the estimated dates themselves, a figure that does not differ significantly among language families. As a resource for further research that may require dates of known level of accuracy, we offer a list of ASJP time depths for nearly all the world's recognized language families and for many subfamilies. The greater the degree of linguistic differentiation within a stock, the greater is the period of time that must be assumed for the development of such differentiations.

The ASJP project aims at establishing relationships between languages on the basis of the Swadesh word list. For this purpose, lists have been collected and phonologically transcribed for almost 3,500 languages. Using a method based on the algorithm proposed by Levenshtein (1966), a custom-made computer program calculates the distances between all pairs of languages in the database. Standard software is used to express the relationships between languages graphically. The current article compares the results of our lexiconbased approach with the results of a similar exercise that takes the typological variables contained in the WALS database as a point of departure. We establish that the latter approach leads to even better results than the lexicon-based one. The best result in terms of correspondence with some well-established genetic and areal classifications, however, is attained when the lexical and typological methods are combined, especially if we select both the most stable Swadesh items and the most stable WALS variables.

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