Using qualia information to identify lexical semantic classes in an unsupervised clustering task
This work addresses the complex problem of acquiring lexical information for natural language processing, but it is incremental as it builds on existing methods for unsupervised clustering.
The paper tackled the problem of identifying lexical semantic classes (HUMAN, LOCATION, EVENT) in English using automatically obtained FORMAL role descriptors in an unsupervised clustering task, showing it is possible to discriminate between classes and account for fine-grained distinctions with ambiguous expressions.
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.