This paper describes the participation of the
USI-UPF team at the shared task of the 2019
Computational Linguistics and Clinical Psychology
Workshop (CLPsych2019). The goal
is to assess the degree of suicide risk of social
media users given a labelled dataset with their
posts. An appropriate suicide risk assessment,
with the usage of automated methods, can
assist experts on the detection of people at
risk and eventually contribute to prevent suicide.
We propose a set of machine learning
models ...
This paper describes the participation of the
USI-UPF team at the shared task of the 2019
Computational Linguistics and Clinical Psychology
Workshop (CLPsych2019). The goal
is to assess the degree of suicide risk of social
media users given a labelled dataset with their
posts. An appropriate suicide risk assessment,
with the usage of automated methods, can
assist experts on the detection of people at
risk and eventually contribute to prevent suicide.
We propose a set of machine learning
models with features based on lexicons, word
embeddings, word level n-grams, and statistics
extracted from users’ posts. The results show
that the most effective models for the tasks are
obtained integrating lexicon-based features, a
selected set of n-grams, and statistical measures.
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