Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for identifying suicidal ideation in phone conversations are difficult to use because of their long duration and noisy nature. |
| Approach: | They propose a self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. |
| Outcome: | The proposed approach outperforms the baseline models in overall performance with an F score of 66.01% and significantly higher F-score in detecting the most dangerous cases. |
Similar Papers
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation (2025.findings-emnlp)
Copied to clipboard
Tong Li, Shu Yang, Junchao Wu, Jiyao Wei, Lijie Hu, Mengdi Li, Derek F. Wong, Joshua R. Oltmanns, Di Wang
| Challenge: | Existing data on suicidal ideation in private conversations are limited . a new dataset of 1,200 test cases is presented to address this gap . |
| Approach: | They propose a dataset of 1,200 test cases simulating implicit suicidal ideation in private contexts. |
| Outcome: | The proposed dataset includes 1,200 test cases simulating implicit suicidal ideation in dialogue scenarios. |
Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Suicide is a global problem, with one suicide case for every 100 deaths worldwide . social networking sites are an essential forum for communication and information sharing . |
| Approach: | This paper compares natural language processing to suicidal ideation detection and risk assessment . it urges better intention understanding for reliable suicide risk assessment with computational methods . |
| Outcome: | This paper compares the performance of natural language processing to suicidal ideation detection and risk assessment tasks. |
Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language (2022.coling-1)
Copied to clipboard
| Challenge: | Existing domain-specific models for detecting suicide are lacking in low-resource languages. |
| Approach: | They propose a model that combines pre-trained language models with a fixed set of suicidal cues and a two-stage fine-tuning process to detect SI. |
| Outcome: | The proposed model outperforms baseline models even early on in the conversation and performs well across genders and age groups. |
Combining Psychological Theory with Language Models for Suicide Risk Detection (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing models for suicide prevention are limited in domains and are not available in low-resource languages. |
| Approach: | They propose a computational model that combines pre-trained language models with a fixed set of manually crafted suicidal cues and a two-stage fine-tuning process to detect suicide risk. |
| Outcome: | The proposed model outperforms baseline models even early on in the conversation and performs well across genders and age groups. |
Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue (2024.lrec-main)
Copied to clipboard
| Challenge: | Contemporary NLP has progressed from feature-based classification to fine-tuning and prompt-based techniques . many of these techniques remain understudied in the context of real-world, clinically enriched spontaneous dialogue. |
| Approach: | They investigate the efficacy and overall performance of a range of NLP techniques on transcribed speech from patients with schizophrenia and other disorders. |
| Outcome: | The proposed methods are effective in analyzing transcribed speech from patients with schizophrenia and healthy controls taking a clinically-validated language test. |
PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. |
| Approach: | They propose a time-and-phase-aware framework that adaptively learns features from a user’s historical emotional spectrum to contextualize suicidal intent. |
| Outcome: | The proposed framework outperforms state-of-the-art methods while outperforming existing methods. |
PsyGUARD: An Automated System for Suicide Detection and Risk Assessment in Psychological Counseling (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems for fine-grained suicide detection and risk assessment are lacking . a lack of domain-specific systems for this task poses a challenge to automated crisis intervention aimed at suicide prevention. |
| Approach: | They propose to use a fine-grained suicide detection system to assess risk in counseling . they develop a taxonomy for detecting suicide ideation and a large-scale dataset . |
| Outcome: | The proposed system detects suicidal ideation and assesses risk in counseling . it can provide safe, helpful, and tailored responses for further assessment . |
A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets (P18-3)
Copied to clipboard
| Challenge: | Suicidal ideation on social media websites is associated with higher suicide rates . suicide is the second leading cause of death among 15-29-year-olds . |
| Approach: | They propose a supervised method for detecting suicidal ideation in tweets using a dataset of manually annotated tweets. |
| Outcome: | The proposed method is compared against four baselines to validate its utility. |
SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media (N19-3)
Copied to clipboard
Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, Rajiv Ratn Shah
| Challenge: | Suicide is a leading cause of death among youth worldwide and currently only uses text-based cues to detect suicidal ideation. |
| Approach: | They propose a deep learning based model to extract text-based features from tweets and a novel Feature Stacking approach to combine other community-based information. |
| Outcome: | The proposed model outperforms existing models on an annotated dataset of tweets using a three-phase strategy and proposes a novel Feature Stacking approach to combine other community-based information such as historical author profiling and graph embeddings. |
Event Detection for Suicide Understanding (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for detecting suicide-related events are limited . recognizing suicide- related events is critical to understanding the condition, authors argue . |
| Approach: | They propose a dataset to detect event trigger words of suicide-related events in forums . they propose 'suicideED' dataset to capture suicidal actions and ideation . |
| Outcome: | The proposed dataset captures suicide actions and ideation, and general risk and protective factors. |