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Modelling selectional preferences in a lexical hierarchy

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

The talk will describe Bayesian selectional preference models that incorporate knowledge from a lexical hierarchy such as WordNet. Inspired by previous work on probabilistic modelling with WordNet, these approaches are based either on “cutting” the hierarchy at an appropriate level of generalisation or on a “walking” model that selects a path from the root to a leaf. In an evaluation comparing against human plausibility judgements, we show that the models presented here outperform previously proposed comparable WordNet-based models, are competitive with state-of-the-art selectional preference models and are particularly well-suited to estimating plausibility for items that were not seen in training.

This talk is part of the NLIP Seminar Series series.

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