KADO@LT-EDI-ACL2022: BERT-based Ensembles for Detecting Signs of Depression from Social Media Text
Depression is a common and serious mental illness that early detection can improve the patient’s symptoms and make depression easier to treat. This paper mainly introduces the relevant content of the task “Detecting Signs of Depression from Social Media Text at DepSign-LT-EDI@ACL-2022”. The goal of DepSign is to classify the signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed” based on social media’s posts. In this paper, we propose a predictive ensemble model that utilizes the fine-tuned contextualized word embedding, ALBERT, DistilBERT, RoBERTa, and BERT base model. We show that our model outperforms the baseline models in all considered metrics and achieves an F1 score of 54% and accuracy of 61%, ranking 5th on the leader-board for the DepSign task.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Detection of depression on social networks using transformers and ensembles
As the impact of technology on our lives is increasing, we witness increased use of social media that became an essential tool not only for communication but also for sharing information with community about our thoughts…
Depression DetectionLanguage ModelingLanguage ModellingTransfer LearningSSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts
Depression is one of the most common mentalissues faced by people. Detecting signs ofdepression early on can help in the treatmentand prevention of extreme outcomes like suicide.Since the advent of the internet, peopleha…
Transfer LearningOPI@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text using RoBERTa Pre-trained Language Models
This paper presents our winning solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI-ACL2022. The task was to create a system that, given social media posts in English, should de…
Depression DetectionLanguage ModelingLanguage ModellingIDIAP Submission@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text
Depression is a common illness involving sadness and lack of interest in all day-to-day activities. It is important to detect depression at an early stage as it is treated at an early stage to avoid consequences. In this…
PositionSSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text
DepSign-LT-EDI@ACL-2022 aims to ascer-tain the signs of depression of a person fromtheir messages and posts on social mediawherein people share their feelings and emo-tions. Given social media postings in English,the sys…
Multi-class Classification