Hybrid Intelligent System of Crisis Assessment using Natural Language Processing and Metagraph Knowledge Base

被引：1

作者：

Kanev, Anton ^{[1
]}

Terekhov, Valery ^{[1
]}

Kochneva, Maria ^{[2
]}

Chernenky, Valery ^{[1
]}

Skvortsova, Maria ^{[1
]}

机构：

[1] Bauman Moscow State Tech Univ, Dept Informat & Control Syst, Moscow, Russia

[2] Wrexham Glyndwr Univ, Comp, Wrexham, Wales

来源：

PROCEEDINGS OF THE 2021 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (ELCONRUS) | 2021年

关键词：

risk classification; metagraph; knowledge base; natural language processing; hybrid intelligent system;

D O I：

10.1109/ElConRus51938.2021.9396100

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The actual task of risk assessment is the ranking of news in accordance with the degree of a conflict situation. One of the metrics for ranking international events is the Goldstein scale. With this metric, each event is compared with the numerical value of conflict or cooperation. The meaning of these phrases may change if they are analyzed with the bag-of-words model. Therefore, the authors propose to search for phrases in texts using a hybrid intelligent system consisting of a natural language processing pipeline and a metagraph knowledge base. A modified TF-IDF metric is used to classify texts by the Goldstein scale. It counts the number of concepts in the knowledge base instead of words. The paper compares the precision of text classification using words and concepts analyzed by natural language processing. The study was performed on the OpenCorpora dataset.

引用

页码：2099 / 2103

页数：5

共 10 条

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