Crowdsourcing Event Extraction

Publication Date: 
Friday, 29 August, 2014
Published in: 
NewsKDD2014 - Data Science for News Publishing Workshop
Aljaz Kosmerlj, Jenya Belyaeva, Gregor Leban, Blaz Fortuna and Marko Grobelnik

We present a system for manually extracting structured event information from freeform news wire text. The extraction is performed on news articles preprocessed by services developed within the XLike project and is guided by suggestions the system produces using machine learning techniques. Results of testing performed using human annotators show the system can produce meaningful data and suggest several avenues for improvement of the system.

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