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.
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Notes
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forgiveness, happiness_peacefulness, love, pride, hopefulness, thankfulness, blame, anger, fear, abuse, sorrow, hopelessness, guilt, information, instructions.
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Dataset available at https://www.iitp.ac.in/ ai-nlp-ml/resources.html#CARES.
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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).
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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.
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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
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