Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Gene name identification and normalization using a model organism database
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Properties and benefits of calibrated classifiers
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Weakly supervised natural language learning without redundant views
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Gene name extraction using FlyBase resources
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Confidence estimation for information extraction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Exploring hedge identification in biomedical literature
Journal of Biomedical Informatics
A joint model for normalizing gene and organism mentions in text
WBIE '09 Proceedings of the Workshop on Biomedical Information Extraction
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A pervasive problem facing many biomedical text mining applications is that of correctly associating mentions of entities in the literature with corresponding concepts in a database or ontology. Attempts to build systems for automating this process have shown promise as demonstrated by the recent BioCreAtIvE Task 1B evaluation. A significant obstacle to improved performance for this task, however, is a lack of high quality training data. In this work, we explore methods for improving the quality of (noisy) Task 1B training data using variants of weakly supervised learning methods. We present positive results demonstrating that these methods result in an improvement in training data quality as measured by improved system performance over the same system using the originally labeled data.