A Random Walk Approach to Selectional Preferences Based on Preference Ranking and Propagation

摘要

This paper presents an unsupervised random walk approach to alleviate data spar-sity for selectional preferences. Based on the measure of preferences between predicates and arguments, the model aggregates all the transitions from a given predicate to its nearby predicates, and propagates their argument preferences as the given predicate's smoothed preferences. Experimental results show that this approach outperforms several state-of-the-art method-s on the pseudo-disambiguation task, and it better correlates with human plausibility judgements.