Artificial Intelligence - Special volume on empirical methods
Applying system combination to base noun phrase identification
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Protein name tagging guidelines: lessons learned: Conference Papers
Comparative and Functional Genomics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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
Protein name tagging for biomedical annotation in text
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Semi-supervised anaphora resolution in biomedical texts
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
Bootstrapping and evaluating named entity recognition in the biomedical domain
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
Biomedical event extraction without training data
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
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The names of named entities very often occur as constituents of larger noun phrases which denote different types of entity. Understanding the structure of the embedding phrase can be an enormously beneficial first step to enhancing whatever processing is intended to follow the named entity recognition in the first place. In this paper, we examine the integration of general purpose linguistic processors together with domain specific named entity recognition in order to carry out the task of baseNP detection. We report a best F-score of 87.17% on this task. We also report an inter-annotator agreement score of 98.8 Kappa on the task of baseNP annotation of a new data set.