Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning

被引:0
作者
Peng, Minlong [1 ]
Xing, Xiaoyu [1 ]
Zhang, Qi [1 ]
Fu, Jinlan [1 ]
Huang, Xuanjing [1 ]
机构
[1] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China
来源
57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019) | 2019年
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task. We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data. A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of the words constituting an entity. This greatly reduces the requirement on the quality of the dictionaries and makes our method generalize well with quite simple dictionaries. Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. We have published the source code at https : // github.com/v-mipeng/LexiconNER.
引用
收藏
页码:2409 / 2419
页数:11
相关论文
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