{"type":"ScholarlyArticle","title":"Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes","authors":[{"name":"Soumitra Ghosh","slug":"soumitra-ghosh","given":"Soumitra","family":"Ghosh","affiliation":"Department of Computer Science and Engineering , IIT Patna , India"},{"name":"Asif Ekbal","slug":"asif-ekbal","given":"Asif","family":"Ekbal","affiliation":"Department of Computer Science and Engineering , IIT Patna , India"},{"name":"Pushpak Bhattacharyya","slug":"pushpak-bhattacharyya","given":"Pushpak","family":"Bhattacharyya","affiliation":"Department of Computer Science and Engineering , IIT Bombay , India"}],"abstract":"The World Health Organization (WHO) has emphasized the importance of significantly accelerating suicide prevention efforts to fulfill the United Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper, we present an end-to-end multitask system to address a novel task of detection of two interpersonal risk factors of suicide, Perceived Burdensomeness (PB) and Thwarted Belongingness (TB) from suicide notes. We also introduce a manually translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the benchmark CEASE-v2.0 dataset, annotated with temporal orientation, PB and TB labels. We exploit the temporal orientation and emotion information in the suicide notes to boost overall performance. For comprehensive evaluation of our proposed method, we compare it to several state-of-the-art approaches on the existing CEASE-v2.0 dataset and the newly announced CoMCEASE-v2.0 dataset. Empirical evaluation suggests that temporal and emotional information can substantially improve the detection of PB and TB.","doi":"10.24963/ijcai.2022/704","url":"https://ijcai.org/proceedings/2022/704","pdf":"https://ijcai.org/proceedings/2022/0704.pdf","markdown":"https://ijcai.org/proceedings/2022/704.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/704","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":704,"pages":{"first":5073,"last":5079},"keywords":["Natural Language Processing: Sentiment Analysis and Text Mining","Humans and AI: Computational Sustainability and Human Well-Being","Natural Language Processing: Resources and Evaluation","Machine Learning: Multi-task and Transfer Learning","Machine Learning: Attention Models"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987151","length_sec":90,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985294","length_sec":722,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. 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