Christopher Parisien

Christopher Parisien
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Main area of research
Natural language & AI

Christopher Parisien is a Principal Research Engineer in the Clinical Language Understanding group with research interests in information extraction, semi-supervised learning, and distributed semantics. Before his work in clinical language, Chris was involved with virtual assistants in Nuance’s NLP Research group. He holds a PhD and MSc in Computational Linguistics from the University of Toronto, and a BMath in Computer Science from the University of Waterloo. Some of his past research includes computational models of child language acquisition, psycholinguistics, and biologically plausible neural networks.

Selected articles

Perspective-taking behavior as the probabilistic weighing of multiple domains

Our starting point is the apparently-contradictory results in the psycholinguistic literature regarding whether, when interpreting a definite referring expressions, listeners process relative to the common

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Generalizing between form and meaning using learned verb classes

Early language development critically depends on the ability to form abstract representations of linguistic knowledge, and to generalize that knowledge to new situations. In verb

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Incorporating coercive constructions into a verb lexicon

We take the first steps towards augmenting a lexical resource, VerbNet, with probabilistic information about coercive constructions. We focus on CAUSED-MOTION as an example construction

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Learning verb alternations in a usage-based Bayesian model

One of the key debates in language acquisition involves the degree to which children’s early linguistic knowledge employs abstract representations. While usage-based accounts that focus

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Modelling the acquisition of verb polysemy in children

We use token-level clustering methods to simulate children’s acquisition of the senses of a polysemous verb. Using actual child-directed data, we show that simple syntactic

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