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        <title>Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task</title>
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        <namePart type="given">Esteban</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task
%A Ríssola, Esteban
%A Ramírez-Cifuentes, Diana
%A Freire, Ana
%A Crestani, Fabio
%Y Niederhoffer, Kate
%Y Hollingshead, Kristy
%Y Resnik, Philip
%Y Resnik, Rebecca
%Y Loveys, Kate
%S Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F rissola-etal-2019-suicide
%X 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.
%R 10.18653/v1/W19-3021
%U https://aclanthology.org/W19-3021
%U https://doi.org/10.18653/v1/W19-3021
%P 167-171

Markdown (Informal)

[Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task](https://aclanthology.org/W19-3021) (Ríssola et al., CLPsych 2019)

• Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task (Ríssola et al., CLPsych 2019)

ACL

• Esteban Ríssola, Diana Ramírez-Cifuentes, Ana Freire, and Fabio Crestani. 2019. Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pages 167–171, Minneapolis, Minnesota. Association for Computational Linguistics.