A New Entity-relation Joint Extraction Model using Reinforcement Learning and Its Application Test

Heping Peng, Zhong Xu, Wenxiong Mo, Yong Wang, Qingdan Huang, Chengzhu Sun, Ting He

2022

Abstract

Extractions of entity and relation are the key part of natural language processing and its application. The current popular entity extraction methods mainly rely on artificially formulated features and domain knowledge which cannot achieve simultaneous extraction of entities and their relations, and are largely affected by noise labeling problems. This paper proposes a new entity-relation extraction model based on reinforcement learning. This model uses the joint extraction tagging strategy in which the sentences are firstly input into a joint extractor based on the Long Short-term Memory network for prediction and subsequently the reinforcement learning algorithm is based on the Policy Gradient for the extraction training. The model is tested on a public application dataset and the experimental results show the validity of the presented joint extraction algorithm.

Paper Citation

in Harvard Style

Peng H., Xu Z., Mo W., Wang Y., Huang Q., Sun C. and He T. (2022). A New Entity-relation Joint Extraction Model using Reinforcement Learning and Its Application Test. In Proceedings of the International Conference on Big Data Economy and Digital Management - Volume 1: BDEDM, ISBN 978-989-758-593-7, pages 992-999. DOI: 10.5220/0011361800003440

in Bibtex Style

@conference{bdedm22,
author={Heping Peng and Zhong Xu and Wenxiong Mo and Yong Wang and Qingdan Huang and Chengzhu Sun and Ting He},
title={A New Entity-relation Joint Extraction Model using Reinforcement Learning and Its Application Test},
booktitle={Proceedings of the International Conference on Big Data Economy and Digital Management - Volume 1: BDEDM,},
year={2022},
pages={992-999},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011361800003440},
isbn={978-989-758-593-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Big Data Economy and Digital Management - Volume 1: BDEDM,
TI - A New Entity-relation Joint Extraction Model using Reinforcement Learning and Its Application Test
SN - 978-989-758-593-7
AU - Peng H.
AU - Xu Z.
AU - Mo W.
AU - Wang Y.
AU - Huang Q.
AU - Sun C.
AU - He T.
PY - 2022
SP - 992
EP - 999
DO - 10.5220/0011361800003440