Authors:
Shengwu Xiong
;
Jingjing Mao
;
Pengfei Duan
and
Shaohao Miao
Affiliation:
Wuhan University of Technology, China
Keyword(s):
DNNs, Relation Extraction, Chinese Geographical Knowledge.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Symbolic Systems
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.