In this article we refine the formulation of the problem of prepositional phrase (PP) attachment as a four-way disambiguation problem. We argue that, in interpreting PPs, both knowledge about the site of the attachment (the traditional noun-verb attachment distinction) and the nature of the attachment (the distinction of arguments from adjuncts) are needed. We introduce a method to learn arguments and adjuncts based on a definition of arguments as a vector of features. In a series of supervised classification experiments, first we explore the features that enable us to learn the distinction between arguments and adjuncts. We find that both linguistic diagnostics of argumenthood and lexical semantic classes are useful. Second, we investigate the best method to reach the four-way classification of potentially ambiguous prepositional phrases. We find that whereas it is overall better to solve the problem as a single four-way classification task, verb arguments are sometimes more precisely identified if the classification is done as a two-step process, first choosing the attachment site and then labeling it as argument or adjunct.
We present experiments aiming at an automatic classification of Spanish verbs into lexical semantic classes. We apply well-known techniques that have been developed for the English language to Spanish, proving that empirical methods can be re-used through languages without substantial changes in the methodology. Our results on subcategorisation acquisition compare favourably to the state of the art for English. For the verb classification task, we use a hierarchical clustering algorithm, and we compare the output clusters to a manually constructed classification.
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