Prepositional Phrase-attachment is a common source of ambiguity in natural language. The previous approaches use limited 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 semantic features as well as unsupervised information 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 provided and 92.85% when semantic information is provided.