Overview of eRisk at CLEF 2019: Early Risk Prediction on the Internet (extended overview)

The task of style change detection aims at detecting whether a given document was written by a single author or by multiple authors.. Also previous PAN editions aimed to

Following 2017 format, the 2018 caption task contains two subtasks, a concept detection subtask that aims at extracting UMLS (Unified Medical Language System R ) Concept

PIR CLEF 2018 has provided registered participants with the data sets originally developed for the PIR-CLEF 2017 Pilot task; the data col- lected are related to real search

users), the second week we gave the 2nd chunk of data (second oldest submissions of all test users), and so forth. After each release, the teams had to process the data and, before

The second learning phase is a classification model that learns patterns from these time series to detect early signs of such mental disorders.. Predicting at-risk users once at the

Regarding the features that are extracted to build predictive models, the most common ones are those related to the users’ texts such as: frequencies of each word or multiple

Similar to the task in 2017, the bag of words ensemble again obtained the best results in the depression subtask, while the CNN based on the self-trained fastText embeddings (BCSGD)

The task consisted of labeling isolated claims or from a political debate or a speech according to their factuality: true, half-true, or false.. We offered the task in both English