AUTHOR=Szmrecsanyi Benedikt , Grafmiller Jason , Rosseel Laura TITLE=Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes JOURNAL=Frontiers in Artificial Intelligence VOLUME=2 YEAR=2019 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2019.00023 DOI=10.3389/frai.2019.00023 ISSN=2624-8212 ABSTRACT=

Inspired by work in comparative sociolinguistics and quantitative dialectometry, we sketch a corpus-based method (Variation-Based Distance & Similarity Modeling—VADIS for short) to rigorously quantify the similarity between varieties and dialects as a function of the correspondence of the ways in which language users choose between different ways of saying the same thing. To showcase the potential of the method, we present a case study that investigates three syntactic alternations in some nine international varieties of English. Key findings include that (a) probabilistic grammars are remarkably similar and stable across the varieties under study; (b) in many cases we see a cluster of “native” (a.k.a. Inner Circle) varieties, such as British English, whereas “non-native” (a.k.a. Outer Circle) varieties, such as Indian English, are a more heterogeneous group; and (c) coherence across alternations is less than perfect.