Paper: Unsupervised Learning of Selectional Restrictions and Detection of Argument Coercions

ACL ID D11-1091
Title Unsupervised Learning of Selectional Restrictions and Detection of Argument Coercions
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

Metonymic language is a pervasive phe- nomenon. Metonymic type shifting, or ar- gument type coercion, results in a selectional restriction violation where the argument’s se- mantic class differs from the class the predi- cate expects. In this paper we present an un- supervised method that learns the selectional restriction of arguments and enables the de- tection of argument coercion. This method also generates an enhanced probabilistic reso- lution of logical metonymies. The experimen- tal results indicate substantial improvements the detection of coercions and the ranking of metonymic interpretations.