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Báo cáo khoa học: "Statistical Models for Unsupervised Prepositional Phrase Attachment"

We present several unsupervised statistical models for the prepositional phrase attachment task that approach the accuracy of the best supervised methods for this task. Our unsupervised approach uses a heuristic based on attachment proximity and trains from raw text that is annotated with only part-of-speech tags and morphological base forms, as opposed to attachment information. It is therefore less resource-intensive and more portable than previous corpus-based algorithm proposed for this task. . | Statistical Models for Unsupervised Prepositional Phrase Attachment Adwait Ratnaparkhi Dept of Computer and Information Science University of Pennsylvania 200 South 33rd Street Philadelphia PA 19104-6389 adwaitOunagi.cis.upenn.edu Abstract We present several unsupervised statistical models for the prepositional phrase attachment task that approach the accuracy of the best supervised methods for this task. Our unsupervised approach uses a heuristic based on attachment proximity and trains from raw text that is annotated with only part-of-speech tags and morphological base forms as opposed to attachment information. It is therefore less resource-intensive and more portable than previous corpus-based algorithm proposed for this task. We present results for prepositional phrase attachment in both English and Spanish. 1 Introduction Prepositional phrase attachment is the task of deciding for a given preposition in a sentence the attachment site that corresponds to the interpretation of the sentence. For example the task in the following examples is to decide whether the preposition with modifies the preceding noun phrase with head word shirt or the preceding verb phrase with head word bought or washed . 1. I bought the shirt with pockets. 2. I washed the shirt with soap. In sentence 1 with modifies the noun shirt since with pockets describes the shirt. However in sentence 2 with modifies the verb washed since with soap describes how the shirt is washed. While this form of attachment ambiguity is usually easy for people to resolve a computer requires detailed knowledge about words e.g. washed vs. bought in order to successfully resolve such ambiguities and predict the correct interpretation. 2 Previous Work Most of the previous successful approaches to this problem have been statistical or corpusbased and they consider only prepositions whose attachment is ambiguous between a preceding noun phrase and verb phrase. Previous work has framed the problem as a classification