Statistical Language Learning
The Journal of Machine Learning Research
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Applying system combination to base noun phrase identification
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Named entity extraction based on a maximum entropy model and transformation rules
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Text chunking by system combination
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
A robust multilingual portable phrase chunking system
Expert Systems with Applications: An International Journal
Named entity recognition in Vietnamese using classifier voting
ACM Transactions on Asian Language Information Processing (TALIP)
Highly accurate error-driven method for noun phrase detection
Pattern Recognition Letters
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
Semi-supervised constituent grammar induction based on text chunking information
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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This paper considers the hypothesis that voting between multiple data representations can be more accurate than voting between multiple learning models This hypothesis has been considered before (cf [San00]) but the focus was on voting methods rather than the data representations In this paper, we focus on choosing specific data representations combined with simple majority voting On the community standard CoNLL-2000 data set, using no additional knowledge sources apart from the training data, we achieved 94.01 Fβ=1 score for arbitrary phrase identification compared to the previous best Fβ=1 93.90 We also obtained 95.23 Fβ=1 score for Base NP identification Significance tests show that our Base NP identification score is significantly better than the previous comparable best Fβ=1 score of 94.22 Our main contribution is that our model is a fast linear time approach and the previous best approach is significantly slower than our system.