Paper: Statistical Models for Unsupervised Prepositional Phrase Attachment

ACL ID P98-2177
Title Statistical Models for Unsupervised Prepositional Phrase Attachment
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998
Authors

We present several unsupervised statistical models for the prepositional phrase attachment task that approach the accuracy of the best su- pervised methods for this task. Our unsuper- vised approach uses a heuristic based on at- tachment 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 pre- vious corpus-based algorithm proposed for this task. We present results for prepositional phrase attachment in both English and Span- ish.