Unsupervised induction of meaningful semantic classes through selectional preferences

Abstract

This paper addresses the general task of semantic class learning by introducing a methodology to induce semantic classes for labeling instances of predicate arguments in an input text. The proposed methodology takes a Proposition Store as Background Knowledge Base to firstly identify a set of classes capable of representing the arguments of predicates in the store; where the classes corresponds to common nouns from the store to support interpretability. Then, it learns a selectional preference model for predicates based on tuples of classes to set up a generative model of propositions from which to perform the induction of classes. The proposed method is completely unsupervised and rely on a reference collection of unlabeled text documents used as the source of background knowledge to build the proposition store. We demonstrate our proposal on a collection of news stories. Specifically, we evaluate the learned model in the task of predicting tuples of argument instances for predicates from held-aside data.