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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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