A neural network approach to selectional preference acquisition

Abstract

This paper investigates the use of neural networks for the acquisition of selectional preferences. Inspired by recent advances of neural network models for NLP applications, we propose a neural network model that learns to discriminate between felicitous and infelicitous arguments for a particular predicate. The model is entirely unsupervised - preferences are learned from unannotated corpus data. We propose two neural network architectures: one that handles standard two-way selectional preferences and one that is able to deal with multi-way selectional preferences. The model's performance is evaluated on a pseudo-disambiguation task, on which it is shown to achieve state of the art performance.