Practical Extraction of Disaster-Relevant Information from Social Media

被引：0

作者：

Imran, Muhammad ^{[1
]}

Elbassuoni, Shady ^{[2
]}

Castillo, Carlos ^{[3
]}

Diaz, Fernando ^{[4
]}

Meier, Patrick ^{[3
]}

机构：

[1] Univ Trento, Trento, Italy

[2] Amer Univ Beirut, Beirut, Lebanon

[3] Qatar Comp, Res Inst, Doha, Qatar

[4] Microsoft Res, Mountain View, CA USA

来源：

PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'13 COMPANION) | 2013年

关键词：

Social Media; information Filtering; information Extraction;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general.

引用

页码：1021 / 1024

页数：4

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