A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Boosting precision and recall of dictionary-based protein name recognition
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
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
Exploiting context for biomedical entity recognition: from syntax to the web
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploring deep knowledge resources in biomedical name recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Unsupervised gene/protein named entity normalization using automatically extracted dictionaries
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Brief Communication: Two-phase biomedical named entity recognition using CRFs
Computational Biology and Chemistry
Recognizing biomedical named entities using skip-chain conditional random fields
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Boosting performance of gene mention tagging system by hybrid methods
Journal of Biomedical Informatics
Using a shallow linguistic kernel for drug-drug interaction extraction
Journal of Biomedical Informatics
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Bio-entity name recognition is the key step for information extraction from biomedical literature. This paper presents a dictionary-based bio-entity name recognition approach. The approach expands the bio-entity name dictionary via the Abbreviation Definitions identifying algorithm, improves the recall rate through the improved edit distance algorithm and adopts some post-processing methods including Pre-keyword and Post-keyword expansion, Part of Speech expansion, merge of adjacent bio-entity names and the exploitation of the contextual cues to further improve the performance. Experiment results show that with this approach even an internal dictionary-based system could achieve a fairly good performance.