The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Effective self-training for parsing
Open accessAbstract
We present a simple, but surprisingly effective, method of self-training a twophase parser-reranker system using readily available unlabeled data.We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker.Our improved model achieves an f -score of 92.1%, an absolute 1.1% improvement (12% error reduction) over the previous best result for Wall Street Journal parsing.Finally, we provide some analysis to better understand the phenomenon.
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Cites
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Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
Charniak, E., & Johnson, M. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219862 · Cited by 872
Self-citationAn empirical study of smoothing techniques for language modeling
Chen, S. F., & Goodman, J. (1996) · Proceedings of the 34th annual meeting on Association for Computational Linguistics - · doi:10.3115/981863.981904 · Cited by 770
Discriminative Reranking for Natural Language Parsing
Collins, M., & Koo, T. (2005) · Computational Linguistics · doi:10.1162/0891201053630273 · Cited by 710
Penn, T. (1999) · Medical Entomology and Zoology · Cited by 652
An Empirical Study of Smoothing Techniques for Language Modeling
Chen, S. F., & Goodman, J. (1996) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9606011 · Cited by 612
Statistical parsing with a context-free grammar and word statistics
Eugene Charniak, D. (1997) · Cited by 521
Structural ambiguity and lexical relations
Hindle, D., & Rooth, M. (1990) · Proceedings of the workshop on Speech and Natural Language - HLT '90 · doi:10.3115/116580.116664 · Cited by 503
Cited by
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Natural Language Processing (almost) from Scratch
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 4,962
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020) · arXiv · doi:10.48550/arxiv.2001.07685 · Cited by 2,892
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference
Schick, T., & Schütze, H. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.20 · Cited by 1,305
Recurrent Neural Network Grammars
Dyer, C., Kuncoro, A., Ballesteros, M., & Smith, N. A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1024 · Cited by 517
Metadata
- Authors
- McClosky, D., Charniak, E., & Johnson, M.
- Venue
- Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -
- Published
- 2006
- Pages
- 152-159
- OpenAlex
- W2163568299
- Cited by
- 594
- References
- 18
- FWCI
- 20.65
- Type
- conference paper
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
