Publikation
Clickbait Identification using Neural Networks
Philippe Thomas
Clickbait Challenge 2017, 2017.
Zusammenfassung
This paper presents the results of our participation in the Clickbait Detection Challenge 2017. The system relies on a fusion of neural networks, incorporating different types of available informations. It does not require any linguistic preprocessing, and hence generalizes more easily to new domains and languages. The final combined model achieves a mean squared error of 0.0428, an accuracy of 0.826, and a F1score of 0.564. According to the official evaluation metric the system ranked 6th of the 13 participating teams.
Projekte
ALL-SIDES - ALL-SIDES: Neue sprachtechnologische Verfahren der Analyse großer Textmengen für die Unterstützung von Entscheidungsprozessen mittels Sozialer Intelligenz,
SD4M - Smart Data For Mobility,
SDW - Smart Data Web
SD4M - Smart Data For Mobility,
SDW - Smart Data Web