Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
High-recall protein entity recognition using a dictionary
Bioinformatics
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
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
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
Expert Systems with Applications: An International Journal
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Boosting performance of gene mention tagging system by hybrid methods
Journal of Biomedical Informatics
Tool wear state recognition based on linear chain conditional random field model
Engineering Applications of Artificial Intelligence
Towards a Protein-Protein Interaction information extraction system: Recognizing named entities
Knowledge-Based Systems
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Biological named entity recognition is a critical task for automatically mining knowledge from biological literature. In this paper, this task is cast as a sequential labeling problem and Conditional Random Fields model is introduced to solve it. Under the framework of Conditional Random Fields model, rich features including literal, context and semantics are involved. Among these features, shallow syntactic features are first introduced, which effectively improve the model's performance. Experiments show that our method can achieve an F-measure of 71.2% in an open evaluation data, which is better than most of state-of-the-art systems.