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Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
Open accessAbstract
Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a coarse-to-fine generative parser (Charniak, 2000). This method generates 50-best lists that are of substantially higher quality than previously obtainable. We used these parses as the input to a MaxEnt reranker (Johnson et al., 1999; Riezler et al., 2002) that selects the best parse from the set of parses for each sentence, obtaining an f-score of 91.0% on sentences of length 100 or less.
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Cited by
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
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Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821
Learning Accurate, Compact, and Interpretable Tree Annotation
Petrov, S., Barrett, L., Thibaux, R., & Klein, D. (2006) · Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06 · doi:10.3115/1220175.1220230 · Cited by 793
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Metadata
- Authors
- Charniak, E., & Johnson, M.
- Venue
- Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05
- Published
- 2005
- Pages
- 173-180
- OpenAlex
- W2125712079
- Cited by
- 872
- References
- 0
- FWCI
- 41.97
- Type
- conference paper
- Language
- en
- Open access
- bronze
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