Year: 2019
Type: article
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).
Source: IEEE Access
Authors: Wei Yuliang, Xin Guodong, Wang Wei, Wang Bailing
Institution: Harbin Institute of Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,750
Award: G3737190111
OpenAlex ID: W2942425488