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 from raw text that is annotated with only part-of-speech tags and morphological 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.
More recently, REF developed an unsupervised method that collects statistics from text annotated with part-of-speech tags and morphological base forms.
Statistical Models for Unsupervised Prepositional Phrase Attachment
COLING-ACL
Computer Science