new/sleak - Information Extraction and Visualization for Investigative Data Journalists

被引：0

作者：

Yimam, Seid Muhie ^{[1
]}

Ulrich, Heiner ^{[2
]}

von Landesberger, Tatiana ^{[3
]}

Rosenbach, Marcel ^{[2
]}

Regneri, Michaela ^{[2
]}

Panchenko, Alexander ^{[1
]}

Lehmann, Franziska ^{[3
]}

Fahrer, Uli ^{[1
]}

Biemann, Chris ^{[1
]}

Ballweg, Kathrin ^{[3
]}

机构：

[1] Tech Univ Darmstadt, Comp Sci Dept, FG Language Technol, Darmstadt, Germany

[2] Tech Univ Darmstadt, Comp Sci Dept, Graph Interact Syst Grp, Darmstadt, Germany

[3] SPIEGEL Verlag, Hamburg, Germany

来源：

PROCEEDINGS OF 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL-2016): SYSTEM DEMONSTRATIONS | 2016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：

We present new/s/leak, a novel tool developed for and with the help of journalists, which enables the automatic analysis and discovery of newsworthy stories from large textual datasets. We rely on different NLP preprocessing steps such named entity tagging, extraction of time expressions, entity networks, relations and metadata. The system features an intuitive web-based user interface based on network visualization combined with data exploring methods and various search and faceting mechanisms. We report the current state of the software and exemplify it with the WikiLeaks PlusD (Cablegate) data.

引用

页码：163 / 168

页数：6

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