Bootstrapping semi-supervised annotation method for potential suicidal messages

Caicedo, Roberto Wellington Acuña; Soriano, José Manuel Gómez; Sasieta, Héctor Andrés Melgar

doi:10.1016/j.invent.2022.100519

Cited by 8 publications

(3 citation statements)

References 58 publications

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“…Yet, the efficacy of LLMs in providing precise, evidence-based support to individuals in suicidal distress has not been empirically validated. Furthermore, LLMs currently lack the ability to collect information vital for diagnosing and managing a patient's health condition [105]. For example, ChatGPT's performance has been found to deteriorate as the complexity of clinical cases increase [104], and research identified in this review demonstrates GPT-powered conversational agents and chatbots can be dangerously slow to escalate high-risk mental health situations for human clinical intervention [48].…”

Section: Clinical Applicationsmentioning

confidence: 99%

Applications of Large Language Models in the Field of Suicide Prevention: A Scoping Review (Preprint)

Holmes,

Tang,

Gupta

et al. 2024

Preprint

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BACKGROUND Prevention of suicide is a global health priority. Around 800,000 individuals die by suicide yearly, and for every death, there are another 20 estimated suicide attempts. Large language models (LLMs) hold the potential to enhance scalable, accessible, and affordable digital services for suicide prevention and self-harm interventions. However, their use also raises clinical and ethical questions that require careful consideration. OBJECTIVE This scoping review aimed to identify emergent trends in applications of LLMs within the field of suicide and self-harm research. Additionally, it summarizes key clinical and ethical considerations relevant to this nascent area of research. METHODS Searches were conducted in four databases. Eligible studies described the application of LLMs for suicide or self-harm prevention, detection, or management. English-language peer-reviewed articles and conference proceedings were included, with no date restrictions. This review adhered to PRISMA-ScR standards. RESULTS Of the 533 studies identified, 36 met inclusion criteria, and an additional 7 more were identified through citation chaining, resulting in a total of 43 studies for review. A narrative synthesis approach was used to synthesize study characteristics, objectives, models, data sources, proposed clinical applications, and ethical considerations. Studies showed a bifurcation of publication fields with varying publication norms between computer science and mental health. While most studies (77%) focused on identifying suicide risk, newer applications leveraging generative functions (e.g., support, education, and training) are emerging. Social media was the most common source of LLM training data. BERT (Bidirectional Encoder Representation Transformer) was the predominant model used, although GPT (Generative Pre-trained Transformer) featured prominently in generative applications. Clinical applications of LLMs were reported in 60% of studies, often for suicide risk detection or as clinical assistance tools. Ethical considerations were reported in 33% of studies, with privacy, confidentiality, and consent strongly represented. CONCLUSIONS This evolving research area, bridging computer science and mental health, demands a multi-disciplinary approach. While open access models and datasets will likely shape this field, documenting their limitations and potential biases is crucial. High-quality training data is essential for refining these models and mitigating unwanted biases. Policies that address ethical concerns – particularly related to privacy and security when using social media data – are imperative. The emergence of generative AI signals a shift in approach, particularly in applications related to care, support, and education. Ongoing human oversight, whether through human-in-the-loop testing or expert external validation, is essential for responsible development and use.

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Section: Clinical Applicationsmentioning

confidence: 99%

Applications of Large Language Models in the Field of Suicide Prevention: A Scoping Review (Preprint)

Holmes,

Tang,

Gupta

et al. 2024

Preprint

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“…Note that annotation of cross-document event coreference was only conducted within a given event cluster, as manual annotation over the entire corpus would be an overwhelming task. A possible solution for this scaling problem could be found in semi-supervised annotation methods, which in recent years have been gaining popularity for large-scale text annotation tasks (Caicedo et al, 2022). However, due to the complexity and intricacies involved in this type of annotation we do not yet esteem these methods as viable for event coreference annotation specifically.…”

Section: Data Annotationmentioning

confidence: 99%

Constructing a cross-document event coreference corpus for Dutch

Langhe

Clercq

Hoste

2022

Lang Resources & Evaluation

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Event coreference resolution is a task in which different text fragments that refer to the same real-world event are automatically linked together. This task can be performed not only within a single document but also across different documents and can serve as a basis for many useful Natural Language Processing applications. Resources for this type of research, however, are extremely limited. We compiled the first large-scale dataset for cross-document event coreference resolution in Dutch, comparable in size to the most widely used English event coreference corpora. As data for event coreference is notoriously sparse, we took additional steps to maximize the number of coreference links in our corpus. Due to the complex nature of event coreference resolution, many algorithms consist of pipeline architectures which rely on a series of upstream tasks such as event detection, event argument identification and argument coreference. We tackle the task of event argument coreference to both illustrate the potential of our compiled corpus and to lay the groundwork for a Dutch event coreference resolution system in the future. Results show that existing NLP algorithms can be easily retrofitted to contribute to the subtasks of an event coreference resolution pipeline system.

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“…Over the past decade, although many studies have been devoted to achieving automatic data annotation [10] using various traditional machine-learning methods or deeplearning algorithms, such as Support Vector Machine (SVM) [11], multi-layer perceptron (MLP) [12], Variational Bayes [13], Decision Trees(DTs) [14], Recurrent Neural Networks (RNNs) [15], etc., the accuracy and universality of annotation still do not meet the requirements of application [16]. However, nowadays, data grow at a geometric rate, and unlabeled nodes are difficult to obtain in the data, which makes it difficult to use supervised learning.…”

Section: Introductionmentioning

confidence: 99%

A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data

Yu,

Zhou,

Cui

2023

Mathematics

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With continuous progress in science and technology, a large amount of data are produced in all fields of the world at anytime and anywhere. These data are unmarked and lack marking information, while manual marking is time-consuming and laborious. Herein, this paper introduces a distributed semi-supervised labeling framework. This framework addresses the issue of missing data by proposing an attribute-filling method based on subspace learning. Furthermore, this paper presents a distributed semi-supervised learning strategy that trains sub-models (private models) within each sub-system. Finally, this paper develops a distributed graph convolutional neural network fusion technique with enhanced interpretability grounded on the attention mechanism. This paper assigns weights of importance to the edges of each layer in the graph neural network based on sub-models and public data, thereby enabling distributed and interpretable graph convolutional attention. Extensive experimentation using public datasets demonstrates the superiority of the proposed scheme over other state-of-the-art baselines, achieving a reduction in loss of 50% compared to the original approach.

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Bootstrapping semi-supervised annotation method for potential suicidal messages

Cited by 8 publications

References 58 publications

Applications of Large Language Models in the Field of Suicide Prevention: A Scoping Review (Preprint)

Applications of Large Language Models in the Field of Suicide Prevention: A Scoping Review (Preprint)

Constructing a cross-document event coreference corpus for Dutch

A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data

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