This paper focuses on problems of attachment of prepositional phrases (PPs) and problems of prepositional suggestions. We transform the problems of PPs attachment and prepositional suggestions into an abstract model, and apply the same computational procedures to solve these two problems. The common model features four headwords, i.e., the verb, the first noun, the preposition, and the second noun in the prepositional phrases. Our methods consider the semantic features of the headwords in WordNet to train classification models, and apply the learned models for tackling the attachment and suggestion problems. This exploration of PP attachment problems is special in that only those PPs that are almost equally possible to attach to the verb and the first noun were used in the study. The proposed models consider only four headwords to achieve satisfactory performances. This study reconfirms that semantic information is instrument for both PP attachment and prepositional suggestions.
Proceedings of the 24th Conference on Computational Linguistics and Speech Processing, ROCLING 2012，32-46