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Abstract: This paper considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that de ne an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how... (Update)
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BibTeX entry: (Update)
M. Collins, 2000. Discriminative Reranking for Natural Language Parsing, Proceedings ICML'2000 , Stanford, Ca. http://citeseer.ist.psu.edu/collins00discriminative.html More
@inproceedings{ collins00discriminative,
author = "Michael Collins",
title = "Discriminative Reranking for Natural Language Parsing",
booktitle = "Proc. 17th International Conf. on Machine Learning",
publisher = "Morgan Kaufmann, San Francisco, CA",
pages = "175--182",
year = "2000",
url = "citeseer.ist.psu.edu/collins00discriminative.html" }
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