Abstract
Inspired by recent advances in emotion-cause extraction in texts and its potential in research on computational studies in suicide motives and tendencies and mental health, we address the problem of cause identification and cause extraction for emotion in suicide notes. We introduce an emotion-cause annotated suicide corpus of 5769 sentences by labeling the benchmark CEASE-v2.0 dataset (4932 sentences) with causal spans for existing annotated emotions. Furthermore, we expand the utility of the existing dataset by adding emotion and emotion cause annotations for an additional 837 sentences collected from 67 non-English suicide notes (Hindi, Bangla, Telugu). Our proposed approaches to emotion-cause identification and extraction are based on pre-trained transformer-based models that attain performance figures of 83.20% accuracy and 0.76 Ratcliff-Obershelp similarity, respectively. The findings suggest that existing computational methods can be adapted to address these challenging tasks, opening up new research areas.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
forgiveness, happiness_peacefulness, love, pride, hopefulness, thankfulness, blame, anger, fear, abuse, sorrow, hopelessness, guilt, information, instructions.
- 5.
Dataset available at https://www.iitp.ac.in/Â ai-nlp-ml/resources.html#CARES.
- 6.
References
Capstick, A.: Recognition of emotional disturbance and the prevention of suicide. BMJ 1(5180), 1179 (1960)
Chen, Y., Hou, W., Cheng, X., Li, S.: Joint learning for emotion classification and emotion cause detection. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pp. 646–651. Association for Computational Linguistics (2018). https://doi.org/10.18653/v1/d18-1066. https://doi.org/10.18653/v1/d18-1066
Chen, Y., Hou, W., Li, S., Wu, C., Zhang, X.: End-to-end dblp:journals/jmlr/srivastavahkss14emotion-cause pair extraction with graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 198–207. International Committee on Computational Linguistics, Barcelona, Spain (Online), December 2020. https://doi.org/10.18653/v1/2020.coling-main.17. https://aclanthology.org/2020.coling-main.17
Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8440–8451. Association for Computational Linguistics, Online, July 2020. https://doi.org/10.18653/v1/2020.acl-main.747. https://aclanthology.org/2020.acl-main.747
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https://doi.org/10.18653/v1/N19-1423. https://aclanthology.org/N19-1423
Ghazi, D., Inkpen, D., Szpakowicz, S.: Detecting emotion stimuli in emotion-bearing sentences. In: Gelbukh, A. (ed.) Computational Linguistics and Intelligent Text Processing. LNCS, vol. 9042, pp. 152–165. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_12
Ghosh, S., Ekbal, A., Bhattacharyya, P.: Cease, a corpus of emotion annotated suicide notes in English. In: Calzolari, N., et al. (eds.) Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, 11–16 May 2020, pp. 1618–1626. European Language Resources Association (2020). https://aclanthology.org/2020.lrec-1.201/
Ghosh, S., Ekbal, A., Bhattacharyya, P.: A multitask framework to detect depression, sentiment and multi-label emotion from suicide notes. Cognitive Computation, pp. 1–20 (2021)
Gui, L., Wu, D., Xu, R., Lu, Q., Zhou, Y.: Event-driven emotion cause extraction with corpus construction. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1639–1649. Association for Computational Linguistics, Austin, November 2016. https://doi.org/10.18653/v1/D16-1170. https://aclanthology.org/D16-1170
Ho, T., Yip, P.S., Chiu, C., Halliday, P.: Suicide notes: what do they tell us? Acta Psychiatr. Scand. 98(6), 467–473 (1998)
Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: SpanBERT: improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguist. 8, 64–77 (2020)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May, 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1412.6980
Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. CoRR abs/1907.11692 (2019). http://arxiv.org/abs/1907.11692
Poria, S., et al.: Recognizing emotion cause in conversations. Cognitive Computation, pp. 1–16 (2021)
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383–2392. Association for Computational Linguistics, Austin, Texas, November 2016. https://doi.org/10.18653/v1/D16-1264. https://aclanthology.org/D16-1264
Russo, I., Caselli, T., Rubino, F., Boldrini, E., MartÃnez-Barco, P.: EMOCause: an easy-adaptable approach to extract emotion cause contexts. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011), pp. 153–160. Association for Computational Linguistics, Portland, Oregon, June 2011. https://aclanthology.org/W11-1720
Shneidman, E.S., Farberow, N.L.: A socio-psychological investigation of suicide. In: Perspectives in Personality Research, pp. 270–293. Springer (1960)
Spitzer, R.L., Cohen, J., Fleiss, J.L., Endicott, J.: Quantification of agreement in psychiatric diagnosis: a new approach. Arch. Gen. Psychiatry 17(1), 83–87 (1967)
Talmy, L.: Toward a Cognitive Semantics, vol. 2. MIT Press (2000)
Wagner, F.: Suicide notes. Danish Med. J. 7, 62–64 (1960)
Xia, R., Ding, Z.: Emotion-cause pair extraction: a new task to emotion analysis in texts. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1003–1012. Association for Computational Linguistics, Florence, Italy, July 2019. https://doi.org/10.18653/v1/P19-1096. https://aclanthology.org/P19-1096
Acknowledgement
Soumitra Ghosh acknowledges the partial support from the project titled ’Development of CDAC Digital Forensic Centre with AI based Knowledge Support Tools’ supported by MeitY, Gov. of India and Gov. of Bihar (project #: P-264).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Ethical Implications
We followed the policies of using the original data and did not violate any copyright issues. The study was deemed exempt by our Institutional Review Board. The codes and data will be made available for research purposes only, after filling and signing an appropriate data compliance form.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ghosh, S., Roy, S., Ekbal, A., Bhattacharyya, P. (2022). CARES: CAuse Recognition for Emotion in Suicide Notes. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-99739-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99738-0
Online ISBN: 978-3-030-99739-7
eBook Packages: Computer ScienceComputer Science (R0)