@inproceedings{f357bab0d51d4ee08830b30a9c1cfe00,
title = "Semi-supervised biomedical relation classification using generalized expectation criteria",
abstract = "Semi-supervised learning is necessary for the extensive application of machine learning in practice. It can use unlabeled data to improve the performance of existing supervised machine learning method. In this work, we addressed the biomedical relation classification problem by utilizing a semi-supervised method which can train Maximum Entropy models according to Generalized Expectation criteria. In the proposed method, instead of instance labeling used in previous works, the feature labeling was applied to get the training data which can save lots of labeling time. A topic model was involved to choose the features for labeling. Experiment results show that the proposed method can dramatically improve the performance of biomedical relation classification through incorporating unlabeled data by feature labeling.",
keywords = "biomedical relation classification, generalized expectation criteria, maximum entropy, semi-supervised learning",
author = "Sun, \{Cheng Jie\} and Lin Yao and Lei Lin and Sha, \{Xue Jun\} and Wang, \{Xiao Long\}",
year = "2011",
doi = "10.1109/ICMLC.2011.6016953",
language = "英语",
isbn = "9781457703065",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "1949--1952",
booktitle = "Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011",
address = "美国",
note = "10th International Conference on Machine Learning and Cybernetics, ICMLC 2011 ; Conference date: 10-07-2011 Through 13-07-2011",
}