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Modeling Context-sensitive Selectional Preference with Distributed Representations

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If you have a question about this talk, please contact Mohammad Taher Pilehvar.

This talk will discuss a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP). CSP models the narrative consistency between the predicate and preceding contexts of its arguments, in addition to the conventional SP based on semantic types. Furthermore, I will present a novel CSP model that extends a neural SP model to incorporate contextual information into the distributed representations of arguments. Experiments show that the proposed CSP model successfully learns CSP and outperforms the conventional SP model in coreference cluster ranking.

This talk is part of the Language Technology Lab Seminars series.

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