Prepositional Phrase Attachment through a Backed-Off Model
| dc.creator | Collins, Michael | |
| dc.creator | Brooks, James | |
| dc.date | 1995-06-22 | |
| dc.date.accessioned | 2026-07-07T09:09:56Z | |
| dc.date.available | 2026-07-07T09:09:56Z | |
| dc.description | Recent work has considered corpus-based or statistical approaches to the problem of prepositional phrase attachment ambiguity. Typically, ambiguous verb phrases of the form {v np1 p np2} are resolved through a model which considers values of the four head words (v, n1, p and n2). This paper shows that the problem is analogous to n-gram language models in speech recognition, and that one of the most common methods for language modeling, the backed-off estimate, is applicable. Results on Wall Street Journal data of 84.5% accuracy are obtained using this method. A surprising result is the importance of low-count events - ignoring events which occur less than 5 times in training data reduces performance to 81.6%. | |
| dc.description | To appear in Proceedings of the Third Workshop on Very Large Corpora, 12 pages, LaTeX | |
| dc.identifier | https://arxiv.org/abs/cmp-lg/9506021 | |
| dc.identifier | http://arxiv.org/abs/cmp-lg/9506021 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/151212 | |
| dc.subject | Computation and Language | |
| dc.title | Prepositional Phrase Attachment through a Backed-Off Model | |
| dc.type | text |