Abstract
This paper provides an overview of eRisk 2018. This was the second year that this lab was organized at CLEF. The main purpose of eRisk was to explore issues of evaluation methodology, effectiveness metrics and other processes related to early risk detection. Early detection technologies can be employed in different areas, particularly those related to health and safety. The second edition of eRisk had two tasks: a task on early risk detection of depression and a task on early risk detection of anorexia.
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References
Losada, D.E., Crestani, F.: A test collection for research on depression and language use. In: Fuhr, N., et al. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 28–39. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44564-9_3
Losada, D.E., Crestani, F., Parapar, J.: eRISK 2017: CLEF lab on early risk prediction on the internet: experimental foundations. In: Jones, G.J.F., et al. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 346–360. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65813-1_30
Acknowledgements
We thank the support obtained from the Swiss National Science Foundation (SNSF) under the project “Early risk prediction on the Internet: an evaluation corpus”, 2015.
We also thank the financial support obtained from the (i)“Ministerio de Economía y Competitividad” of the Government of Spain and FEDER Funds under the research project TIN2015-64282-R, (ii) Xunta de Galicia (project GPC 2016/035), and (iii) Xunta de Galicia – “Consellería de Cultura, Educación e Ordenación Universitaria” and the European Regional Development Fund (ERDF) through the following 2016–2019 accreditations: ED431G/01 (“Centro singular de investigacion de Galicia”) and ED431G/08.
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Losada, D.E., Crestani, F., Parapar, J. (2018). Overview of eRisk: Early Risk Prediction on the Internet. In: Bellot, P., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2018. Lecture Notes in Computer Science(), vol 11018. Springer, Cham. https://doi.org/10.1007/978-3-319-98932-7_30
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DOI: https://doi.org/10.1007/978-3-319-98932-7_30
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