Statistical Models for Unsupervised Prepositional Phrase Attachment

We present several unsupervised statistical models for the prepositional phrase attachment task that approach the accuracy of the best supervised methods for this task. Our unsupervised approach uses a heuristic based on attachment proximity and trains h'om raw text that is annotated with only part-oi;speech tags and morphologicM base forms, as opposed to attachment information. It is therefore less resource-intensive and more portable than previous corpus-based algorithm proposed for this task. We present results for prepositional phrase attachment in both English and Spanish.

• Type: Journal
• Year: 1998
• Where: CORR
• Authors: Adwait Ratnaparkhi
• Keywords: CORR 1998, Education, Prepositional Phrase, Prepositional Phrase Attachment, Unsupervised Statistical Models