The Journal of Machine Learning Research
Natural Language Engineering
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th 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
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
Use of support vector learning for chunk identification
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
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Artificial Intelligence in Medicine
Medical entity recognition: a comparison of semantic and statistical methods
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Using an ensemble system to improve concept extraction from clinical records
Journal of Biomedical Informatics
Methodological Review: Approaches to verb subcategorization for biomedicine
Journal of Biomedical Informatics
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts
Journal of Biomedical Informatics
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Text chunking is an essential pre-processing step in information extraction systems. No comparative studies of chunking systems, including sentence splitting, tokenization and part-of-speech tagging, are available for the biomedical domain. We compared the usability (ease of integration, speed, trainability) and performance of six state-of-the-art chunkers for the biomedical domain, and combined the chunker results in order to improve chunking performance. We investigated six frequently used chunkers: GATE chunker, Genia Tagger, Lingpipe, MetaMap, OpenNLP, and Yamcha. All chunkers were integrated into the Unstructured Information Management Architecture framework. The GENIA Treebank corpus was used for training and testing. Performance was assessed for noun-phrase and verb-phrase chunking. For both noun-phrase chunking and verb-phrase chunking, OpenNLP performed best (F-scores 89.7% and 95.7%, respectively), but differences with Genia Tagger and Yamcha were small. With respect to usability, Lingpipe and OpenNLP scored best. When combining the results of the chunkers by a simple voting scheme, the F-score of the combined system improved by 3.1 percentage point for noun phrases and 0.6 percentage point for verb phrases as compared to the best single chunker. Changing the voting threshold offered a simple way to obtain a system with high precision (and moderate recall) or high recall (and moderate precision). This study is the first to compare the performance of the whole chunking pipeline, and to combine different existing chunking systems. Several chunkers showed good performance, but OpenNLP scored best both in performance and usability. The combination of chunker results by a simple voting scheme can further improve performance and allows for different precision-recall settings.