939 resultados para 379999 Studies in Human Society not elsewhere classified


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Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.

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In this paper we investigate the concepts of 'face' and 'politeness'. We introduce a metalanguage which we believe is a framework for simplifying the analysis of 'face' and 'politeness'. This metalanguage is based on the observation that both 'face' and 'politeness' involve external evaluations of people. This common element is represented in the metalanguage as B what A shows A thinks of B and what B thinks A thinks of B. The implications of the metalanguage for the analysis of Chinese mian and lion ('face') and English face are then discussed. This is followed by an analysis of examples of politeness in English and teineisa ('politeness') in Japanese. We conclude that the metalanguage may be further developed for use in comparisons of 'face' and 'politeness' across cultures. (C) 2003 Elsevier B.V. All rights reserved.