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主动学习与自学习的中文命名实体识别
引用本文: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,2014,36(4):82-88.
作者姓名:钟志农  刘方驰  吴烨  伍江江
作者单位:国防科技大学 电子科学与工程学院,国防科技大学 电子科学与工程学院,国防科技大学 电子科学与工程学院,国防科技大学电子科学与工程学院
基金项目:国家高技术研究发展计划(863计划)主题项目(2011AA120300);湖南省自然科学基金资助项目(11JJ4028 )
摘    要:命名实体识别是信息抽取中的一项基础性任务,如何利用丰富的未标注语料来提高实体识别的指标是该领域一个重要的研究方向。基于条件随机场提出一种将主动学习与自学习相结合的方法——SACRF,通过设置置信度函数和2-Gram频度阈值来选取样本,并采用人工与自动相结合的方式进行标注来扩展训练语料。实验表明,该方法在提高实体识别的精确率和召回率的同时,能够显著地降低人工标注的工作量。

关 键 词:主动学习  自学习  条件随机场  命名实体识别
收稿时间:2013/10/11 0:00:00

Chinese Named Entity Recognition Combined Active Learning with Self-Training
ZHONG Zhinong,LIU Fangchi,WU Ye and WU Jiangjiang.Chinese Named Entity Recognition Combined Active Learning with Self-Training[J].Journal of National University of Defense Technology,2014,36(4):82-88.
Authors:ZHONG Zhinong  LIU Fangchi  WU Ye and WU Jiangjiang
Institution:College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
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 combined self-training with active learning based on CRF (SACRF) is proposed, which selects samples by setting the threshold of confidence and 2-Gram frequency, and expands the training set by annotating the unlabeled corpus manually and automatically. The experiments reveal that this approach can not only improve the precision and recall of NER system, but also reduce the manually annotation efforts greatly.
Keywords:active learning  self-training  conditional random fields  named entity recognition
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