Complex Named Entities Extraction on the Web: Application to Social Events

Armel Fotsoh, Annig Le Parc Lacayrelle, Christian Sallaberry


In this paper, we focus on the extraction of social events in text from the web. We consider social events as complex Named Entities (NEs) i.e. NEs represented by a list of properties that can be simple values (text, number, etc.), ”elementary” NEs and/or other complex NEs. Regarding the extraction of these complex NEs, our contribution focuses on the noisy context issue. Very few works in the state-of-the-art deal with this issue, and the few existing ones have limits in several contexts. We propose an original processing method based on supervised learning and patterns that makes it possible to focus property annotation on specific blocks of webpages. This process is generic and independent of the type of NE processed. We experimented and evaluated it with an example of complex NEs: social events. It appears that, in a noisy context, the results obtained with our approach considerably improve the standard process used in the state-of-the-art. The work was conducted with the objective of generalize it for other categories of complex NEs.


Information extraction, complex named entities, social event

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