PATRICIA—Practical Algorithm To Retrieve Information Coded in Alphanumeric
Journal of the ACM (JACM)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Two-phase biomedical NE recognition based on SVMs
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
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
Mining semantically related terms from biomedical literature
ACM Transactions on Asian Language Information Processing (TALIP)
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
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
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We propose an unsupervised method to automatically extract domain-specific prefixes and suffixes from biological corpora based on the use of PATRICIA tree. The method is evaluated by integrating the extracted affixes into an existing learning-based biological term annotation system. The system based on our method achieves comparable experimental results to the original system in locating biological terms and exact term matching annotation. However, our method improves the system efficiency by significantly reducing the feature set size. Additionally, the method achieves a better performance with a small training data set. Since the affix extraction process is unsupervised, it is assumed that the method can be generalized to extract domain-specific affixes from other domains, thus assisting in domain-specific concept recognition.