Resolving a Pragmatic Prepositional Phrase Attachment Ambiguity

1. Introduction To resolve or not to resolve, that is the structural ambiguity dilemma. The traditional wisdom is to disambiguate only when it matters in terms of the meaning of the utterance, and to do so using the computationally least costly information. NLP work on PP-attachment has followed this wisdom, and much effort has been focused on formulating structural and lexical strategies for resolving noun-phrase and verb-phrase (NP-PP vs. VP-PP) attachment ambiguity (e.g. [8, 11]). In one study, statistical analysis of the distribution of lexical items in a very large text yielded 78% correct parses while two humans achieved just 85%[5]. The close performance of machine and human led the authors to pose two issues that will be addressed in this paper: is the predictive power of distributional data due to "a complementation relation, a modification relation, or something else", and what characterizes the attachments that escape prediction?