An Event Element Extraction Method for Chinese Text

Xia Jing, Wei Zhang, Jingjing Wang, Yongli Wang

2022

Abstract

With the rapid development of computer technology and Internet scale, how to extract useful information from the growing mass of network information and present it in the form of structured text is particularly important. As a solution to this problem, information extraction technology has attracted much attention. Among them, event extraction is an important research direction in the field of information extraction, and it is also one of the most challenging tasks. There are some problems in traditional event extraction methods, such as easy to ignore the context information and insufficient extraction of key features. In order to solve the above problems, this paper uses deep learning method to study the event extraction of Chinese text, and proposes the recognition and classification of Chinese event elements, that is, the detection of Chinese event elements. Using the type information and the corresponding location information of event trigger words, the text vector is obtained as the input of BiLSTM network layer. The attention layer is added on the basis of BiLSTM network layer to better obtain the information of event elements around the trigger words. Finally, the detection results of event elements are obtained through softmax layer output.

Paper Citation

in Harvard Style

Jing X., Zhang W., Wang J. and Wang Y. (2022). An Event Element Extraction Method for Chinese Text. In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI; ISBN 978-989-758-620-0, SciTePress, pages 619-626. DOI: 10.5220/0011753300003607

in Bibtex Style

@conference{icpdi22,
author={Xia Jing and Wei Zhang and Jingjing Wang and Yongli Wang},
title={An Event Element Extraction Method for Chinese Text},
booktitle={Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI},
year={2022},
pages={619-626},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011753300003607},
isbn={978-989-758-620-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI
TI - An Event Element Extraction Method for Chinese Text
SN - 978-989-758-620-0
AU - Jing X.
AU - Zhang W.
AU - Wang J.
AU - Wang Y.
PY - 2022
SP - 619
EP - 626
DO - 10.5220/0011753300003607
PB - SciTePress