Paper: Unsupervised Discovery Of A Statistical Verb Lexicon

ACL ID W06-1601
Title Unsupervised Discovery Of A Statistical Verb Lexicon
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006

This paper demonstrates how unsupervised tech- niques can be used to learn models of deep linguis- tic structure. Determining the semantic roles of a verb’s dependents is an important step in natural language understanding. We present a method for learning models of verb argument patterns directly from unannotated text. The learned models are sim- ilar to existing verb lexicons such as VerbNet and PropBank, but additionally include statistics about the linkings used by each verb. The method is based on a structured probabilistic model of the do- main, and unsupervised learning is performed with the EM algorithm. The learned models can also be used discriminatively as semantic role labelers, and when evaluated relative to the PropBank anno- tation, the best learned model reduces 28% of th...