Vendredi 28 janvier, 15h00, DS-1950
Abstract : This paper explores the emergence of language from the perspectives of usage-based approaches and of complex systems (CS). One of the mysteries of language development is that each of us as learners has had different language experiences and yet somehow we have converged on broadly the same language system. From diverse, often noisy samples, we end up with similar linguistic competence. How can that be ? There must be some constraints in our estimation of how language works. Some views hold that the constraints are in the learner, as expectations of linguistic universals pre-programmed in some form of innate language acquisition device. Others hold that the constraints are in the dynamics of language itself – that language form, language meaning, and language use come together to promote robust induction by means of statistical learning over limited samples. The research described here explores this question with regard English verbs, their grammatical form, semantics, and patterns of usage. It exemplifies CS principles such as agent-based emergence and the importance of scale-free distributions, and CS methods such as distributional analysis, connectionist modeling, and networks analysis.
Bio : Nick Ellis is a Professor of Psychology, Professor of Linguistics, Research Scientist in the English Language Institute, and Associated Faculty at the Centre for the Study of Complex Systems at the University of Michigan. His research interests include language acquisition, cognition, emergentism, corpus linguistics, cognitive linguistics, and psycholinguistics. His research in second language acquisition concerns (1) explicit and implicit language learning and their interface, (2) usage-based acquisition and the probabilistic tuning of the system, (3) vocabulary and phraseology, and (4) learned attention and language transfer. His emergentist research concerns include language as a complex adaptive system, networks analysis of language, scale-free linguistic distributions and robust learning, and computational modeling.