![]() ![]() ![]() Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May, 2015, Conference Track Proceedings (2015). ![]() Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. ![]() Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: SpanBERT: improving pre-training by representing and predicting spans. Ho, T., Yip, P.S., Chiu, C., Halliday, P.: Suicide notes: what do they tell us? Acta Psychiatr. Association for Computational Linguistics, Austin, November 2016. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. Gui, L., Wu, D., Xu, R., Lu, Q., Zhou, Y.: Event-driven emotion cause extraction with corpus construction. Ghosh, S., Ekbal, A., Bhattacharyya, P.: A multitask framework to detect depression, sentiment and multi-label emotion from suicide notes. European Language Resources Association (2020). (eds.) Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, 11–, pp. Ghosh, S., Ekbal, A., Bhattacharyya, P.: Cease, a corpus of emotion annotated suicide notes in English. (ed.) Computational Linguistics and Intelligent Text Processing. Ghazi, D., Inkpen, D., Szpakowicz, S.: Detecting emotion stimuli in emotion-bearing sentences. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. 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. ĭevlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. Association for Computational Linguistics, Online, July 2020. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. Ĭonneau, A., et al.: Unsupervised cross-lingual representation learning at scale. International Committee on Computational Linguistics, Barcelona, Spain (Online), December 2020. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. Ĭhen, Y., Hou, W., Li, S., Wu, C., Zhang, X.: End-to-end dblp:journals/jmlr/srivastavahkss14emotion-cause pair extraction with graph convolutional network. Association for Computational Linguistics (2018). (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pp. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. BMJ 1(5180), 1179 (1960)Ĭhen, Y., Hou, W., Cheng, X., Li, S.: Joint learning for emotion classification and emotion cause detection. KeywordsĬapstick, A.: Recognition of emotional disturbance and the prevention of suicide. The findings suggest that existing computational methods can be adapted to address these challenging tasks, opening up new research areas. 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. 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). 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. 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. ![]()
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