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Found 8 papers, 0 papers with code
DeepBlues@LT-EDI-ACL2022: Depression level detection modelling through domain specific BERT and short text Depression classifiers
In addition we can use short text classifiers to extract relevant text from the long text and achieve slightly better accuracy, albeit, trading off with the processing time for extracting such excerpts.
Deep Temporal Modelling of Clinical Depression through Social Media Text
We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts.
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach
In our work, we describe a Semi-supervised Learning (SSL) framework which uses an initial supervised learning model that leverages 1) a state-of-the-art large mental health forum text pre-trained language model further fine-tuned on a clinician annotated DSD dataset, 2) a Zero-Shot learning model for DSD, and couples them together to harvest depression symptoms related samples from our large self-curated Depression Tweets Repository (DTR).
A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language
We start with a rich word embedding pre-trained from a large general dataset, which is then augmented with embeddings learned from a much smaller and more specific domain dataset through a simple non-linear mapping mechanism.
STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text
We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data scarcity, i. e. Depression Symptoms Detection (DSD) from text.
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model
Multi-label emotion classification is an important task in NLP and is essential to many applications.
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers.
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
Most of the existing methods treat this task as a problem of single-label multi-class text classification.
