Language Evolution and Computation Bibliography

Explaining the diversity of languages across the world is one of the central aims of typological, historical, and evolutionary linguistics. We consider the effect of language contact-the number of non-native speakers a language has-on the way languages change and evolve. By analysing hundreds of languages within and across language families, regions, and text types, we show that languages with greater levels of contact typically employ fewer word forms to encode the same information content (a property we refer to as lexical diversity). Based on three types of statistical analyses, we demonstrate that this variance can in part be explained by the impact of non-native speakers on information encoding strategies. Finally, we argue that languages are information encoding systems shaped by the varying needs of their speakers. Language evolution and change should be modeled as the co-evolution of multiple intertwined adaptive systems: On one hand, the structure of human societies and human learning capabilities, and on the other, the structure of language.

We present evidence that, for English, ambiguity is an active factor in the choice of relativization strategy and that, in speech, prosody plays a role in resolution of ambiguity over the internal role of the relativized constituent. The evidence is based on (semi-)automatic analysis and comparison of automatically-parsed written and spoken portions of the British National Corpus (BNC, Leech, 1992) and of the prosodically-transcribed Spoken English Corpus (SEC, Taylor and Knowles, 1988). The results are evaluated with respect to a model of parsing complexity and syntactic disambiguation (Briscoe 1987, 2000) building on Combinatory Categorial Grammar (Steedman, 2000) and this model is in turn motivated by an evolutionary account of linguistic coevolutionary adaptation of the syntactic and phonological prosodic systems to a solution which minimizes processing cost. To our knowledge this is the first work which investigates linguistic adaptations aimed at reducing ambiguity while making testable predictions about linguistic organization.