A Dataset for Research on Modelling Depression Severity in Online Forum Data

Arachchige, Isuri Anuradha Nanomi; Jayasuriya, Vihangi Himaya; Weerasinghe, Ruvan

doi:10.26615/issn.2603-2821.2021_020

Cited by 2 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the appearance of the transformer-based architecture, the NLP domain witnessed the emergence of several pre-trained language models such as BERT and XLNet [19]. These pre-trained Language models proved good accuracy levels in both sentence-level [2] and token-level [18] techniques in different NLP tasks.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Transformer-based Language models for the Identification of Idiomatic Expressions

Arachchige¹,

Suraweera²,

Herath³

2022

Proceedings of the International Conference EUROPHRAS 2022 (Short Papers, Posters and MUMTTT Workshop Contributions)

View full text Add to dashboard Cite

Idiomatic expressions are part of everyday speech in many languages and text genres. However, identifying idiomatic expressions can be problematic for different NLP tasks as their meaning cannot be easily inferred from their constituting words. Although the automatic identification and understanding of idiomatic expressions are essential for Natural Language Understanding tasks, they are still largely underinvestigated. Until recently, the complex nature and lack of large datasets have prevented the development of machine learning approaches for identifying the idiomatic expressions. With the advancement of machine learning techniques, transformer-based language models such as DistilBERT, RoBERTa, and their variants have shown state of the art performance by capturing the compositionality of the textual representations many NLP tasks. In spite of the progress, these vector representations fail to identify the multiword expressions (MWEs) such as idioms. This study demonstrate that transformer-based language models using contextual embeddings perform much better than existing approaches when applied to texts associated with depression. The core of this article presents two subtasks: (a) binary classification or idiomatic expression identification using BERT sentence embeddings and (b) task based on the analysis of the idiomatic expression usage in depressive textual contents.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Aligning to the contribution of the paper, depressive forum post dataset [19] were used for the evaluation of the models. The dataset was comprising forum posts collected from depression online support forums.…”

Section: Datasetsmentioning

confidence: 99%

Transformer-based Language models for the Identification of Idiomatic Expressions

Arachchige¹,

Suraweera²,

Herath³

2022

Proceedings of the International Conference EUROPHRAS 2022 (Short Papers, Posters and MUMTTT Workshop Contributions)

View full text Add to dashboard Cite

show abstract

“…Technological advances in recent years have led to a significant amount of data and techniques such as sentiment analysis and opinion mining to analyse what people say or share in their everyday life [75][76][77]. As it is, there are both good and bad aspects of technology dependence.…”

Section: Implication Of Future Researchmentioning

confidence: 99%

Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

2021

Self Cite

View full text Add to dashboard Cite

Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to the stigma related to mental health. The digital footprint we all leave behind, particularly in online support forums, provides a window for clinicians to observe and assess such behaviour in order to make potential mental health diagnoses. Natural language processing (NLP) and Machine learning (ML) techniques are able to bridge the existing gaps in converting language to a machine-understandable format in order to facilitate this. Our objective is to undertake a systematic review of the literature on NLP and ML approaches used for depression identification on Online Support Forums (OSF). A systematic search was performed to identify articles that examined ML and NLP techniques to identify depression disorder from OSF. Articles were selected according to the PRISMA workflow. For the purpose of the review, 29 articles were selected and analysed. From this systematic review, we further analyse which combination of features extracted from NLP and ML techniques are effective and scalable for state-of-the-art Depression Identification. We conclude by addressing some open issues that currently limit real-world implementation of such systems and point to future directions to this end.

show abstract

A Dataset for Research on Modelling Depression Severity in Online Forum Data

Cited by 2 publications

References 16 publications

Transformer-based Language models for the Identification of Idiomatic Expressions

Transformer-based Language models for the Identification of Idiomatic Expressions

Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

Contact Info

Product

Resources

About