@inproceedings{gonzalez-hevia-etal-2019-analyzing,
title = "Analyzing the use of existing systems for the {CLP}sych 2019 Shared Task",
author = "Gonz{\'a}lez Hevia, Alejandro and
Cerezo Men{\'e}ndez, Rebeca and
Gayo-Avello, Daniel",
editor = "Niederhoffer, Kate and
Hollingshead, Kristy and
Resnik, Philip and
Resnik, Rebecca and
Loveys, Kate",
booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3017",
doi = "10.18653/v1/W19-3017",
pages = "148--151",
abstract = "In this paper we describe the UniOvi-WESO classification systems proposed for the 2019 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. We explore the use of two systems trained with ReachOut data from the 2016 CLPsych task, and compare them to a baseline system trained with the data provided for this task. All the classifiers were trained with features extracted just from the text of each post, without using any other metadata. We found out that the baseline system performs slightly better than the pretrained systems, mainly due to the differences in labeling between the two tasks. However, they still work reasonably well and can detect if a user is at risk of suicide or not.",
}
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<abstract>In this paper we describe the UniOvi-WESO classification systems proposed for the 2019 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. We explore the use of two systems trained with ReachOut data from the 2016 CLPsych task, and compare them to a baseline system trained with the data provided for this task. All the classifiers were trained with features extracted just from the text of each post, without using any other metadata. We found out that the baseline system performs slightly better than the pretrained systems, mainly due to the differences in labeling between the two tasks. However, they still work reasonably well and can detect if a user is at risk of suicide or not.</abstract>
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%0 Conference Proceedings
%T Analyzing the use of existing systems for the CLPsych 2019 Shared Task
%A González Hevia, Alejandro
%A Cerezo Menéndez, Rebeca
%A Gayo-Avello, Daniel
%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 gonzalez-hevia-etal-2019-analyzing
%X In this paper we describe the UniOvi-WESO classification systems proposed for the 2019 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. We explore the use of two systems trained with ReachOut data from the 2016 CLPsych task, and compare them to a baseline system trained with the data provided for this task. All the classifiers were trained with features extracted just from the text of each post, without using any other metadata. We found out that the baseline system performs slightly better than the pretrained systems, mainly due to the differences in labeling between the two tasks. However, they still work reasonably well and can detect if a user is at risk of suicide or not.
%R 10.18653/v1/W19-3017
%U https://aclanthology.org/W19-3017
%U https://doi.org/10.18653/v1/W19-3017
%P 148-151
Markdown (Informal)
[Analyzing the use of existing systems for the CLPsych 2019 Shared Task](https://aclanthology.org/W19-3017) (González Hevia et al., CLPsych 2019)
ACL