Using qualia information to identify lexical semantic classes in an unsupervised clustering task

TL;DR

This paper demonstrates that formal role descriptors can effectively classify nouns into semantic classes in an unsupervised manner, even capturing fine-grained distinctions and handling data sparsity.

Contribution

It introduces a novel approach using automatically obtained formal role descriptors for unsupervised lexical semantic classification, validated on English nouns.

Findings

Successful discrimination of semantic classes using formal role information

Effective handling of ambiguous expressions within classes

Filtering and bootstrapping reduce data sparsity and noise

Abstract

Acquiring lexical information is a complex problem, typically approached by relying on a number of contexts to contribute information for classification. One of the first issues to address in this domain is the determination of such contexts. The work presented here proposes the use of automatically obtained FORMAL role descriptors as features used to draw nouns from the same lexical semantic class together in an unsupervised clustering task. We have dealt with three lexical semantic classes (HUMAN, LOCATION and EVENT) in English. The results obtained show that it is possible to discriminate between elements from different lexical semantic classes using only FORMAL role information, hence validating our initial hypothesis. Also, iterating our method accurately accounts for fine-grained distinctions within lexical classes, namely distinctions involving ambiguous expressions. Moreover, a filtering and bootstrapping strategy employed in extracting FORMAL role descriptors proved to minimize effects of sparse data and noise in our task.