Statistical Models for Unsupervised Prepositional Phrase Attachment

dc.creatorRatnaparkhi, Adwait
dc.date1998-07-22
dc.date.accessioned2026-07-07T02:36:19Z
dc.date.available2026-07-07T02:36:19Z
dc.descriptionWe 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 algorithms proposed for this task. We present results for prepositional phrase attachment in both English and Spanish.
dc.descriptionuses colacl.sty
dc.identifierhttps://arxiv.org/abs/cmp-lg/9807011
dc.identifierhttp://arxiv.org/abs/cmp-lg/9807011
dc.identifierProceedings of the 17th International Conference on Computational Linguistics (COLING-ACL '98)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/15875
dc.subjectComputation and Language
dc.titleStatistical Models for Unsupervised Prepositional Phrase Attachment
dc.typetext

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