Statistical Models for Unsupervised Prepositional Phrase Attachment
<|reference_start|>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 algorithms proposed for this task. We present results for prepositional phrase attachment in both English and Spanish.<|reference_end|>
@article{ratnaparkhi1998statistical,
    title={Statistical Models for Unsupervised Prepositional Phrase Attachment},
    author={Adwait Ratnaparkhi (University of Pennsylvania)},
    journal={Proceedings of the 17th International Conference on Computational
  Linguistics (COLING-ACL '98)},
    year={1998},
    archivePrefix={arXiv},
    eprint={cmp-lg/9807011},
    primaryClass={cmp-lg cs.CL}
}