Explaining away ambiguity: Learning verb selectional preference with Bayesian networks

TL;DR

This paper introduces a Bayesian network model for unsupervised learning of verb selectional preferences, effectively handling word sense ambiguity and outperforming existing systems in disambiguation tasks.

Contribution

It proposes a novel Bayesian network approach leveraging WordNet hierarchy and explaining away to improve unsupervised verb sense disambiguation.

Findings

Outperforms state-of-the-art unsupervised disambiguation systems

Effectively handles word sense ambiguity using explaining away

Discusses computational improvements and broader implementation methods

Abstract

This paper presents a Bayesian model for unsupervised learning of verb selectional preferences. For each verb the model creates a Bayesian network whose architecture is determined by the lexical hierarchy of Wordnet and whose parameters are estimated from a list of verb-object pairs found from a corpus. ``Explaining away'', a well-known property of Bayesian networks, helps the model deal in a natural fashion with word sense ambiguity in the training data. On a word sense disambiguation test our model performed better than other state of the art systems for unsupervised learning of selectional preferences. Computational complexity problems, ways of improving this approach and methods for implementing ``explaining away'' in other graphical frameworks are discussed.