A maximum entropy approach to natural language processing
Computational Linguistics
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Two-phase biomedical NE recognition based on SVMs
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
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
Word folding: taking the snapshot of words instead of the whole
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Semantic Classification of Bio-Entities Incorporating Predicate-Argument Features
IEICE - Transactions on Information and Systems
Stacked ensemble coupled with feature selection for biomedical entity extraction
Knowledge-Based Systems
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In this paper, we present a two-phase biomedical NE-recognition method based on a ME model: we first recognize biomedical terms and then assign appropriate semantic classes to the recognized terms. In the two-phase NE-recognition method, the performance of the term-recognition phase is very important, because the semantic classification is performed on the region identified at the recognition phase. In this study, in order to improve the performance of term recognition, we try to incorporate lexical knowledge into pre- and postprocessing of the term-recognition phase. In the preprocessing step, we use domain-salient words as lexical knowledge obtained by corpus comparison. In the postprocessing step, we utilize χ2-based collocations gained from Medline corpus. In addition, we use morphological patterns extracted from the training data as features for learning the ME-based classifiers. Experimental results show that the performance of NE-recognition can be improved by utilizing such lexical knowledge.