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

来源：

IEEE ACCESS | 2019年 / 7卷

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

页码：51648 / 51655

页数：8