Unsupervised Crisis Information Extraction from Twitter Data

被引：0

作者：

Interdonato, Roberto ^{[1
]}

Doucet, Antoine ^{[2
]}

Guillaume, Jean-Loup ^{[2
]}

机构：

[1] CIRAD, UMR TETIS, F-34000 Montpellier, France

[2] Univ La Rochelle, L3i, F-17000 La Rochelle, France

来源：

2018 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM) | 2018年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

While microblogging-based Online Social Networks have become an attractive data source in emergency situations, overcoming information overload is still not trivial. We propose a framework which integrates natural language processing and clustering techniques in order to produce a ranking of relevant tweets based on their informativeness. Experiments on four Twitter collections in two languages (English and French) proved the significance of our approach.

引用

页码：579 / 580

页数：2

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