Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
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
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In this paper, we propose a term identification system using conditional random fields (CRFs) on two biomedical datasets. Through employing several sets of experiments, we make a comprehensive investigation for different types of features. The final experimental results reflect that with carefully designed features i.e., adding not only the individual and dynamic features but also the combinational features, our system can identify biomedical terms with fairly high accuracy on both datasets, compared with other top systems already published in the literature.