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Probabilistic models of similarity and plausibility in context

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If you have a question about this talk, please contact Ekaterina Kochmar.

The distributional approach in its many guises is the most popular paradigm for current research on lexical semantics. In this talk I’ll describe a framework for distributional semantics based on latent variable probabilistic models of co-occurrence (aka “topic models”). These models can answer a variety of semantic questions about how a word interacts with its context; I will focus on questions about co-occurrence plausibility and about similarity between words in the disambiguating context of a sentence or syntactic structure. Modelling plausibility corresponds to the well-known task of selectional preference learning; in-context similarity is fundamental to disambiguation tasks such as lexical substitution. I will show that relatively simple topic models give very good performance across a range of lexical semantic evaluation settings.

This talk is part of the NLIP Seminar Series series.

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