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

Abstract

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).