Active Learning for Uneven Noisy Labeled Data in Mention-Level Relation Extraction

被引:1
作者
Wei Yuliang [1 ]
Xin Guodong [1 ]
Wang Wei [1 ]
Wang Bailing [1 ]
机构
[1] Harbin Inst Technol, Harbin 264209, Heilongjiang, Peoples R China
关键词
Relation extraction; active learning; text mining; deep learning;
D O I
10.1109/ACCESS.2019.2911889
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Mention-level relation extraction (mRE) plays an important role in extracting relational information from short texts such as those exchanged in a social network. Deep learning (DL) has made remarkable achievements; the main problem encountered with DL in mRE is a lack of training samples. In this paper, we present a design for a quick sample-marking method. First, we construct an uneven noisy labeled data (UNLD) set using a pattern matching algorithm, and then a relabeling framework is put forward for modifying the UNLD. With regard to the accuracy, the recall rates of categories with sufficient samples increased from 0.4 to nearly 1 using the relabeling framework. We have released our code and other resources for further research (https://github.com/curtainsky/UNLD).
引用
收藏
页码:51648 / 51655
页数:8
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