Speaker: Professor Stephen Clark, University of Cambridge Computer Laboratory.
Title: Evaluating Compositional Distributed Semantic Models with RELPRON
In this talk I will describe a new dataset designed to test the capabilities of compositional semantic models based on vector spaces. The dataset, called RELPRON, consists of pairs of terms and properties, such as telescope : device that astronomer uses. The idea is that a good compositional model will produce a vector representation of the property which is close to the vector for the term. I will also survey a number of possible approaches to composing vectors, before describing the methods based on neural networks that I have been investigating. It turns out that, in line with many existing datasets, vector addition provides a very challenging baseline for RELPRON, but we are able to improve on the baseline by finding appropriate training data for modelling the semantics of the relative pronoun.