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Towards a Stochastic Model of Linguistic Competence

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In recent years computational linguists, psycholinguists, and even some theoretical linguists have adoped a probabilistic view of linguistic knowledge. The primary motivation for this approach is a concern to incorporate the gradient effects and soft, defeasible constraints evident in speakers’ variable judgements on acceptability into the theory of linguistic competence. On this view knowledge of a language is identified directly with a language model and the probability distribution over the strings of a language that it specifies. I will take up some of the problems involved in developing a viable stochastic representation of competence and suggest possible solutions to these problems. I will also look at the connections between probabilistic theories of learning and a stochastic model of grammar. Finally, I will consider several consequences that such a model has for the competence-performance distinction.

This talk is part of the NLIP Seminar Series series.

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