Probabilistic Feature Grammars (1997)
| Venue: | In Proceedings of the International Workshop on Parsing Technologies |
| Citations: | 35 - 0 self |
BibTeX
@INPROCEEDINGS{Goodman97probabilisticfeature,
author = {Joshua Goodman},
title = {Probabilistic Feature Grammars},
booktitle = {In Proceedings of the International Workshop on Parsing Technologies},
year = {1997},
pages = {89--100}
}
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Abstract
We present a new formalism, probabilistic feature grammar (PFG). PFGs combine most of the best properties of several other formalisms, including those of Collins, Magerman, and Charniak, and in experiments have comparable or better performance. PFGs generate features one at a time, probabilistically, conditioning the probabilities of each feature on other features in a local context. Because the conditioning is local, efficient polynomial time parsing algorithms exist for computing inside, outside, and Viterbi parses. PFGs can produce probabilities of strings, making them potentially useful for language modeling. Precision and recall results are comparable to the state of the art with words, and the best reported without words. 1 Introduction Recently, many researchers have worked on statistical parsing techniques which try to capture additional context beyond that of simple probabilistic context-free grammars (PCFGs), including Magerman (1995), Charniak (1996), Collins (1996; 1997), ...
