A statistical decision making method: A case study on prepositional phrase attachment

Statistical classification methods usually rely on a single best model to make accurate predictions. Such a model aims to maximize accuracy by balancing precision and recall. The Model Switching method as presented in this paper performs with higher predictive accuracy and 100% recall by using a set of decomposable models instead of a single one. The implemented system, MSI, is tested on a case study, predicting Prepositional Phrase Attachment (PPA). The results show that it is more accurate than other statistical techniques that select single models for classification and competitive with other successful NLP approaches in PPA disambiguation. The Model Switching method may be preferable to other methods because of its generality (i.e., wide range of applicability), and its competitive accuracy in prediction. It may also be used as an analytical tool to investigate the nature of the domain and the characteristics of the data with the help of generated models.