The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Incremental parsing with the perceptron algorithm
Open accessAbstract
This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set as that of an existing generative model (Roark, 2001a), and experimental results show that it gives competitive performance to the generative model on parsing the Penn treebank. We demonstrate that training a perceptron model to combine with the generative model during search provides a 2.1 percent F-measure improvement over the generative model alone, to 88.8 percent.
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Cites
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Collins, M. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118694 · Cited by 1,862
Self-citationLarge margin classification using the perceptron algorithm
Freund, Y., & Schapire, R. E. (1998) · Proceedings of the eleventh annual conference on Computational learning theory · doi:10.1145/279943.279985 · Cited by 1,071
Inducing features of random fields
Della Pietra, S., Della Pietra, V., & Lafferty, J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.588021 · Cited by 1,036
An Efficient Boosting Algorithm for Combining Preferences
Freund, Y., Iyer, R. D., Schapire, R. E., & Singer, Y. (1998) · Cited by 806
Discriminative Reranking for Natural Language Parsing
Collins, M., & Koo, T. (2005) · Computational Linguistics · doi:10.1162/0891201053630273 · Cited by 710
Self-citationCollins, M., & Duffy, N. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073128 · Cited by 506
Self-citationPCFG models of linguistic tree representations
Johnson, M. (1998) · Computational Linguistics · doi:10.5555/972764.972768 · Cited by 350
Probabilistic Top-Down Parsing and Language Modeling
Roark, B. (2001) · Computational Linguistics · doi:10.1162/089120101750300526 · Cited by 329
Self-citationRiezler, S., King, T. H., Kaplan, R. M., Crouch, R., Maxwell, J. T., & Johnson, M. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073129 · Cited by 279
Cited by
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Bengio, S., Vinyals, O., Jaitly, N., & Shazeer, N. (2015) · arXiv · doi:10.48550/arxiv.1506.03099 · Cited by 1,621
Sequence-to-Sequence Learning as Beam-Search Optimization
Wiseman, S., & Rush, A. M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1137 · Cited by 481
Search-based structured prediction
Daumé, H., Langford, J., & Marcu, D. (2009) · Machine Learning · doi:10.1007/s10994-009-5106-x · Cited by 465
Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., & Hinton, G. (2014) · arXiv · doi:10.48550/arxiv.1412.7449 · Cited by 464
Modeling Joint Entity and Relation Extraction with Table Representation
Miwa, M., & Sasaki, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1200 · Cited by 369
Metadata
- Authors
- Collins, M., & Roark, B.
- Venue
- Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04
- Published
- 2004
- Pages
- 111-es
- OpenAlex
- W2104917081
- Cited by
- 373
- References
- 17
- FWCI
- 9.32
- Type
- conference paper
- Language
- en
- Open access
- bronze
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