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Abstract: Traditional approaches to language processing have been based on explicit, discrete representations which are difficult to learn from a reasonable linguistic environment---hence, it has come to be accepted that much of our linguistic representations and knowledge is innate. With its focus on learning based upon graded, malleable, distributed representations, connectionist modeling has reopened the question of what could be learned from the environment in the absence of detailed innate knowledge. This paper provides an overview of connectionist models of language processing, at both the lexical and sentence levels.

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