Disambiguating ambiguous biomedical terms in biomedical narrative text: an unsupervised method
Computers and Biomedical Research
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Tuning support vector machines for biomedical named entity recognition
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Tagging gene and protein names in full text articles
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Using name-internal and contextual features to classify biological terms
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Term identification in the biomedical literature
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Country wise classification of human names
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
NLPXML '06 Proceedings of the 5th Workshop on NLP and XML: Multi-Dimensional Markup in Natural Language Processing
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Syntactic and semantic disambiguation of numeral strings using an n-gram method
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Heuristic and rule-based knowledge acquisition: classification of numeral strings in text
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
Comparison of numeral strings interpretation: rule-based and feature-based n-gram methods
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Hi-index | 0.00 |
The classification task is an integral part of named entity extraction. This task has not received much attention in the biomedical setting, partly due to the fact that protein name recognition has been the focus of the majority of the work in this field. We study this problem and focus on different sources of information that can be utilized for the classification task and investigate the extent of their contributions for classification in this domain. However, while developing a specific algorithm for the classification of the names is not our main focus, we make use of some simple techniques to investigate different sources of information and verify our intuitions about their usefulness.