Paper: PP-Attachment Disambiguation Using Large Context

ACL ID H05-1035
Title PP-Attachment Disambiguation Using Large Context
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005

Prepositional Phrase-attachment is a com- mon source of ambiguity in natural lan- guage. The previous approaches use lim- ited information to solve the ambiguity – four lexical heads – although humans disambiguate much better when the full sentence is available. We propose to solve the PP-attachment ambiguity with a Support Vector Machines learning model that uses complex syntactic and seman- tic features as well as unsupervised in- formation obtained from the World Wide Web. The system was tested on several datasets obtaining an accuracy of 93.62% on a Penn Treebank-II dataset; 91.79% on a FrameNet dataset when no manually- annotated semantic information is pro- vided and 92.85% when semantic infor- mation is provided. 1 Problem description 1.1 PP-attachment ambiguity problem Preposit...