Analyzing the use of existing systems for the CLPsych 2019 Shared Task

  1. González Hevia, Alejandro 1
  2. Cerezo Menéndez, Rebeca 1
  3. Gayo-Avello, Daniel 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Actas:
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

Editorial: Association for Computational Linguistics

ISBN: 978-1-948087-95-7

Año de publicación: 2019

Páginas: 148-151

Congreso: Sixth Workshop on Computational Linguistics and Clinical Psychology: Reconciling Outcomes, NAACL 2019, June 6th, Minneapolis, MN

Tipo: Aportación congreso

DOI: 10.18653/V1/W19-3017 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

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.

€ Ver financiación

Información de financiación

This work is partially funded by the Spanish Ministry of Economy and Competitiveness (Society challenges: TIN2017-88877-R).

Financiadores

Referencias bibliográficas

  • Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. Tensorflow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.
  • Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450.
  • Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR, abs/1412.3555.
  • Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Herv´e J´egou. 2017. Word translation without parallel data. arXiv preprint arXiv:1710.04087.
  • Matthew Honnibal and Ines Montani. 2017. spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing. To appear.
  • Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2017. Unsupervised machinetranslation using monolingual corpora only. arXiv preprint arXiv:1711.00043.
  • Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In International conference on machine learning, pages 11881196.
  • David N Milne, Glen Pink, Ben Hachey, and Rafael A Calvo. 2016. Clpsych 2016 shared task: Triaging content in online peer-support forums. In Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology, pages 118–127.
  • Saif Mohammad. 2018. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 174–184. Association for Computational Linguistics.
  • F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12:2825–2830.
  • Han-Chin Shing, Suraj Nair, Ayah Zirikly, Meir Friedenberg, Hal Daum´e III, and Philip Resnik. 2018. Expert, crowdsourced, and machine assessment of suicide risk via online postings. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 25–36.
  • Ayah Zirikly, Philip Resnik, ¨Ozlem Uzuner, and Kristy Hollingshead. 2019. CLPsych 2019 shared task: Predicting the degree of suicide risk in Reddit posts. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic.