Chinese Geographical Knowledge Entity Relation Extraction via Deep Neural Networks

Shengwu Xiong, Jingjing Mao, Pengfei Duan, Shaohao Miao

2017

Abstract

Aiming at the problem of complex relation pattern and low relation extraction precision in the unstructured free text, in this paper, a novel extraction model for Chinese geographical knowledge relation extraction using a real end-to-end deep neural networks (DNNs) is proposed. The proposed method is a fusion DNNs consisting of one convolutional neural networks and two neural networks, which contains word feature, sentence feature and class feature. For the experiments, we construct geographic entity relation type system and corpus. We achieve a good performance with the averaged overall precision of 96.54%, averaged recall of 92.99%, and averaged F value of 94.56%. Experimental results confirm the superiority of the proposed Chinese geographical knowledge relation extraction method. The data of this paper can be obtained from http://nlp.webmeteor.cn.

Paper Citation

in Harvard Style

Xiong S., Mao J., Duan P. and Miao S. (2017). Chinese Geographical Knowledge Entity Relation Extraction via Deep Neural Networks . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 24-33. DOI: 10.5220/0006109700240033

in Bibtex Style

@conference{icaart17,
author={Shengwu Xiong and Jingjing Mao and Pengfei Duan and Shaohao Miao},
title={Chinese Geographical Knowledge Entity Relation Extraction via Deep Neural Networks},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={24-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006109700240033},
isbn={978-989-758-220-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Chinese Geographical Knowledge Entity Relation Extraction via Deep Neural Networks
SN - 978-989-758-220-2
AU - Xiong S.
AU - Mao J.
AU - Duan P.
AU - Miao S.
PY - 2017
SP - 24
EP - 33
DO - 10.5220/0006109700240033