Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
Text classification using string kernels
The Journal of Machine Learning Research
Named entity recognition: a maximum entropy approach using global information
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Named Entity Extraction using AdaBoost
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Named entity recognition with a maximum entropy approach
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Structure and semantics for expressive text kernels
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Kernel methods, syntax and semantics for relational text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Re-ranking models based-on small training data for spoken language understanding
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Convolution kernels on constituent, dependency and sequential structures for relation extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Effective use of WordNet semantics via kernel-based learning
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Combined syntactic and semantic Kernels for text classification
ECIR'07 Proceedings of the 29th European conference on IR research
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Kernel engineering for fast and easy design of natural language applications
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Kernel Engineering for Fast and Easy Design of Natural Language Applications
End-to-end relation extraction using distant supervision from external semantic repositories
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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We present novel kernels based on structured and unstructured features for reranking the N-best hypotheses of conditional random fields (CRFs) applied to entity extraction. The former features are generated by a polynomial kernel encoding entity features whereas tree kernels are used to model dependencies amongst tagged candidate examples. The experiments on two standard corpora in two languages, i.e. the Italian EVALITA 2009 and the English CoNLL 2003 datasets, show a large improvement on CRFs in F-measure, i.e. from 80.34% to 84.33% and from 84.86% to 88.16%, respectively. Our analysis reveals that both kernels provide a comparable improvement over the CRFs baseline. Additionally, their combination improves CRFs much more than the sum of the individual contributions, suggesting an interesting kernel synergy.