国防科技大学学报Issue(4)：82-88,7.DOI:10.11887/j.cn.201404015
主动学习与自学习的中文命名实体识别
Chinese named entity recognition combined active learning with self-training
摘要
Abstract
Named Entity Recognition (NER)is a basic task in information extraction,and it is an important research direction in this domain to use the abundant unlabeled corpus to improve the performance of NER system.An approach combining self-training with active learning based on CRF (SACRF)is proposed.It selected samples by setting the threshold of confidence and 2-Gram frequency,and expanded the training set by annotating the unlabeled corpus manually and automatically.The experiments revealed that this approach can not only improve the precision and recall of NER system,but also reduce the manually annotation efforts greatly.关键词
主动学习/自学习/条件随机场/命名实体识别Key words
active learning/self-training/conditional random fields/named entity recognition分类
信息技术与安全科学引用本文复制引用
钟志农,刘方驰,吴烨,伍江江..主动学习与自学习的中文命名实体识别[J].国防科技大学学报,2014,(4):82-88,7.基金项目
国家高技术研究发展计划主题项目（2011AA120300）；湖南省自然科学基金资助项目 （）
