Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Named Entity recognition without gazetteers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Extracting the names of genes and gene products with a hidden Markov model
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
HLT '91 Proceedings of the workshop on Speech and Natural Language
Effective adaptation of a Hidden Markov Model-based named entity recognizer for biomedical domain
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Bio-medical entity extraction using Support Vector Machines
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Boosting performance of bio-entity recognition by combining results from multiple systems
Proceedings of the 5th international workshop on Bioinformatics
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
How to make the most of NE dictionaries in statistical NER
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach
International Journal of Bioinformatics Research and Applications
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
Towards a Protein-Protein Interaction information extraction system: Recognizing named entities
Knowledge-Based Systems
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In this paper, we report the adaptation of a named entity recognition (NER) system to the biomedical domain in order to participate in the "Shared Task Bio-Entity Recognition". The system is originally developed for German NER that shares characteristics with the biomedical task. To facilitate adaptability, the system is knowledge-poor and utilizes unlabeled data. Investigating the adaptability of the single components and the enhancements necessary, we get insights into the task of bio-entity recognition.