  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 algorithms proposed for this task. We present results for
prepositional phrase attachment in both English and Spanish.
