Automatic Discovery of Telic and Agentive Roles from Corpus Data

We present two methods for automatically discovering the telic and agentive roles of nouns from corpus data. These relations form part of the qualia structure assumed in generative lexicon theory, where the telic role represents a typical purpose of the entity and the agentive role represents the origin of the entity. The first discovery method uses hand-generated templates for each role type, and the second employs a supervised machine-learning technique. To evaluate the effectiveness of the two methods, we took a sample of 30 nouns, selected 50 verbs for each, and then generated a ranked list of verbs for a given noun. Using a variant of Spearman’s rank correlation, we demonstrate the ability of the methods to identify qualia structure.