학술논문
DPRL: Labeling Relation Based on Distant Supervision and POS Rule
Document Type
Conference
Author
Source
2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE) Consumer Electronics and Computer Engineering (ICCECE), 2022 2nd International Conference on. :63-67 Jan, 2022
Subject
Language
Abstract
The lack of high-quality labeled data is one of the main challenges to a lot of relation extraction tasks. Distant supervision introduces many noises, while labeling data manually is costly. In this paper, we design a novel labeling rule called the POS rule. POS rule improves the precision of relation extraction by considering the position, ontology, and semantic of relations. We propose a POS rule mining and optimization framework to generate high-quality labeled data. Experiments on real datasets show that the proposed discovery algorithms can find high-quality POS rules which accurately label training data for relation extraction.