Paper: An Unsupervised Approach To Prepositional Phrase Attachment Using Contextually Similar Words

ACL ID P00-1014
Title An Unsupervised Approach To Prepositional Phrase Attachment Using Contextually Similar Words
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2000
Authors

Prepositional phrase attachment is a common source of ambiguity in natural language processing. We present an unsupervised corpus-based approach to prepositional phrase attachment that achieves similar performance to supervised methods. Unlike previous unsupervised approaches in which training data is obtained by heuristic extraction of unambiguous examples from a corpus, we use an iterative process to extract training data from an automatically parsed corpus. Attachment decisions are made using a linear combination of features and low frequency events are approximated using contextually similar words.