8 citations · 18 across the 12 of their papers we have counts for
13 papers
Towards a physics-informed multiscale digital twin for precision medicine in Alzheimer's disease
Aida Nonn, Elma Kerz, Daniel Wiechmann +1
Deciphering the drivers of brain ageing and neurodegeneration, and explaining heterogeneity in individual trajectories, remains a central challenge for precision medicine. We propo…
MindSET: Advancing Mental Health Benchmarking through Large-Scale Social Media Data
Saad Mankarious, Ayah Zirikly, Daniel Wiechmann +3
Social media data has become a vital resource for studying mental health, offering real-time insights into thoughts, emotions, and behaviors that traditional methods often miss. Pr…
Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data
D. Wiechmann, E. Kempa, E. Kerz +1
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental health condition affecting both children and adults, yet it remains severely underdiagnosed. Recent advances i…
MANTIS at #SMM4H 2023: Leveraging Hybrid and Ensemble Models for Detection of Social Anxiety Disorder on Reddit
Sourabh Zanwar, Daniel Wiechmann, Yu Qiao +1
This paper presents our system employed for the Social Media Mining for Health 2023 Shared Task 4: Binary classification of English Reddit posts self-reporting a social anxiety dis…
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders
Xiaofei Li, Daniel Wiechmann, Yu Qiao +1
In this paper we present our contribution to the TSAR-2022 Shared Task on Lexical Simplification of the EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability.…
(Psycho-)Linguistic Features Meet Transformer Models for Improved Explainable and Controllable Text Simplification
Yu Qiao, Xiaofei Li, Daniel Wiechmann +1
State-of-the-art text simplification (TS) systems adopt end-to-end neural network models to directly generate the simplified version of the input text, and usually function as a bl…
Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media
Sourabh Zanwar, Daniel Wiechmann, Yu Qiao +1
In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing…
Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features
Sourabh Zanwar, Daniel Wiechmann, Yu Qiao +1
In recent years, there has been increased interest in building predictive models that harness natural language processing and machine learning techniques to detect emotions from va…
Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features
Elma Kerz, Yu Qiao, Sourabh Zanwar +1
Research at the intersection of personality psychology, computer science, and linguistics has recently focused increasingly on modeling and predicting personality from language use…
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns
Daniel Wiechmann, Yu Qiao, Elma Kerz +1
There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading. While promising results have been obtained…
Prediction of Listener Perception of Argumentative Speech in a Crowdsourced Dataset Using (Psycho-)Linguistic and Fluency Features
Yu Qiao, Sourabh Zanwar, Rishab Bhattacharyya +4
One of the key communicative competencies is the ability to maintain fluency in monologic speech and the ability to produce sophisticated language to argue a position convincingly.…
Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models
Yu Qiao, Xuefeng Yin, Daniel Wiechmann +1
In this paper, we combined linguistic complexity and (dis)fluency features with pretrained language models for the task of Alzheimer's disease detection of the 2021 ADReSSo (Alzhei…
The Impact of ASR on the Automatic Analysis of Linguistic Complexity and Sophistication in Spontaneous L2 Speech
Yu Qiao, Wei Zhou, Elma Kerz +1
In recent years, automated approaches to assessing linguistic complexity in second language (L2) writing have made significant progress in gauging learner performance, predicting h…