Improving PP Attachment Disambiguation in a Rule-based Parser

This paper deals with how to enhance the performance of a rule-based parser using statistical Information. PP (Prepositional Phrase) attachment ambiguity is one of the main ambiguities found in parsing. We therefore conducted some experiments on extracting statistical information for PP attachment from a corpus, and on applying such information to a rule-based parser. Two types of information are used: supervised learning data and unsupervised learning data. In this paper, we show how we apply these types of information and to what degree they contribute to the PP attachment as well as to the overall parsing performance. The final results show a 5.42% performance improvement in PP attachment, with an 8.7% error reduction ratio in the overall parsing performance.