The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron
Open accessAbstract
This paper introduces new learning algorithms for natural language processing based on the perceptron algorithm. We show how the algorithms can be efficiently applied to exponential sized representations of parse trees, such as the "all subtrees" (DOP) representation described by (Bod 1998), or a representation tracking all sub-fragments of a tagged sentence. We give experimental results showing significant improvements on two tasks: parsing Wall Street Journal text, and named-entity extraction from web data.
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Cites
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,308
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-citationHead-Driven Statistical Models for Natural Language Parsing
Collins, M. (2003) · Computational Linguistics · doi:10.1162/089120103322753356 · Cited by 1,826
Self-citation(1999) · Cited by 1,521
Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning
Айзерман, М. А. (1964) · Automation and Remote Control · Cited by 1,485
A Maximum Entropy Model for Part-Of-Speech Tagging
Ratnaparkhi, A. (1996) · Cited by 1,317
Convolution kernels on discrete structures
Haussler, D. H. (1999) · Cited by 1,089
Cited by
Shallow parsing with conditional random fields
Sha, F., & Pereira, F. (2003) · Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03 · doi:10.3115/1073445.1073473 · Cited by 1,246
Cutting-plane training of structural SVMs
Joachims, T., Finley, T., & Yu, C.-N. J. (2009) · Machine Learning · doi:10.1007/s10994-009-5108-8 · Cited by 958
MaltParser: A language-independent system for data-driven dependency parsing
NIVRE, J., HALL, J., NILSSON, J., CHANEV, A., ERYİGİT, G., KÜBLER, S., MARINOV, S., & MARSI, E. (2007) · Natural Language Engineering · doi:10.1017/s1351324906004505 · Cited by 801
Discriminative Reranking for Natural Language Parsing
Collins, M., & Koo, T. (2005) · Computational Linguistics · doi:10.1162/0891201053630273 · Cited by 710
Self-citationFalse rumors detection on Sina Weibo by propagation structures
Wu, K., Yang, S., & Zhu, K. Q. (2015) · 2015 IEEE 31st International Conference on Data Engineering · doi:10.1109/icde.2015.7113322 · Cited by 585
Metadata
- Authors
- Collins, M., & Duffy, N.
- Venue
- Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02
- Published
- 2001
- Pages
- 263
- OpenAlex
- W2131297983
- Cited by
- 506
- References
- 21
- FWCI
- 9.61
- Type
- conference paper
- Language
- en
- Open access
- bronze
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