The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Kernel methods for relation extraction
Open accessAbstract
We present an application of kernel methods to extracting relations from unstructured natural language sources. We introduce kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels. We use the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting person-affiliation and organization-location relations from text. We experimentally evaluate the proposed methods and compare them with feature-based learning algorithms, with promising results.
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Horn, R. A., & Johnson, C. R. (1985) · Cited by 22,203
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
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
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026683 · Cited by 7,999
A maximum entropy approach to natural language processing
Berger, A. L., Pietra, V. J. D., & Pietra, S. A. D. (1996) · doi:10.5555/234285.234289 · Cited by 3,065
Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., & Haussler, D. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.10.906 · Cited by 2,443
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A Frustratingly Easy Approach for Entity and Relation Extraction
Zhong, Z., & Chen, D. (2020) · arXiv · doi:10.48550/arxiv.2010.12812 · Cited by 531
Attention Guided Graph Convolutional Networks for Relation Extraction
Guo, Z., Zhang, Y., & Lu, W. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1024 · Cited by 522
Relation Extraction: Perspective from Convolutional Neural Networks
Nguyen, T. H., & Grishman, R. (2015) · Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing · doi:10.3115/v1/w15-1506 · Cited by 486
A Comprehensive Survey on Automatic Knowledge Graph Construction
Zhong, L., Wu, J., Li, Q., Peng, H., & Wu, X. (2024) · ACM Computing Surveys · doi:10.1145/3618295 · Cited by 382
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
- Zelenko, D., Aone, C., & Richardella, A.
- Venue
- Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02
- Published
- 2002
- Volume
- 10
- Pages
- 71-78
- OpenAlex
- W2162590473
- Cited by
- 573
- References
- 28
- FWCI
- 5.71
- Type
- conference paper
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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