Paper: A Neural Network Approach to Selectional Preference Acquisition

ACL ID D14-1004
Title A Neural Network Approach to Selectional Preference Acquisition
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

This paper investigates the use of neural networks for the acquisition of selectional preferences. Inspired by recent advances of neural network models for NLP applica- tions, we propose a neural network model that learns to discriminate between felici- tous and infelicitous arguments for a par- ticular predicate. The model is entirely un- supervised ? preferences are learned from unannotated corpus data. We propose two neural network architectures: one that han- dles standard two-way selectional prefer- ences 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 perfor- mance.