Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
Noun phrase recognition by system combination
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A default first order family weight determination procedure for WPDV models
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
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
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
Learning rules and their exceptions
The Journal of Machine Learning Research
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
Text chunking using regularized Winnow
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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
Filtering-Ranking Perceptron Learning for Partial Parsing
Machine Learning
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
Efficient text chunking using linear kernel with masked method
Knowledge-Based Systems
Comparing and combining chunkers of biomedical text
Journal of Biomedical Informatics
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Voting between multiple data representations for text chunking
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
| Hi-index | 0.00 |
In this paper I describe the application of the WPDV algorithm to the CoNLL-2000 shared task, the identification of base chunks in English text (Tjong Kim Sang and Buchholz, 2000). For this task, I use a three-stage architecture: I first run five different base chunkers, then combine them and finally try to correct some recurring errors. Except for one base chunker, which uses the memory-based machine learning system TiMBL, all modules are based on WPDV models (van Halteren, 2000a).