3 citations · 13 across the 10 of their papers we have counts for
11 papers · 1 filter
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…
OpenICL: An Open-Source Framework for In-context Learning
Zhenyu Wu, YaoXiang Wang, Jiacheng Ye +4
In recent years, In-context Learning (ICL) has gained increasing attention and emerged as the new paradigm for large language model (LLM) evaluation. Unlike traditional fine-tuning…
Content Rating Classification for Fan Fiction
Yu Qiao, James Pope
Content ratings can enable audiences to determine the suitability of various media products. With the recent advent of fan fiction, the critical issue of fan fiction content rating…
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.…
Detecting Levels of Depression in Text Based on Metrics
Ashwath Kumar Salimath, Robin K Thomas, Sethuram Ramalinga Reddy +1
Depression is one of the most common and a major concern for society. Proper monitoring using devices that can aid in its detection could be helpful to prevent it all together. The…