Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Text Mining in Radiology Reports
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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This paper introduces machine learning methods for extracting targets and attributes and identifying associations among them from radiology reports written in Korean and English. In the target extraction task, conditional random fields are utilized with language and domain specific features. In the task of finding an association between a target and an attribute, a simple method of generating negative examples from positive examples is introduced and experimented with three different statistical classifiers.