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Found 20 papers, 2 papers with code
A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN
We demonstrate that our approach is promising as it achieves similar results on these two datasets as the best performing black box models reported in the literature.
SPADE: A Big Five-Mturk Dataset of Argumentative Speech Enriched with Socio-Demographics for Personality Detection
In recent years, there has been increasing interest in automatic personality detection based on language.
Language that Captivates the Audience: Predicting Affective Ratings of TED Talks in a Multi-Label Classification Task
The aim of the paper is twofold: (1) to automatically predict the ratings assigned by viewers to 14 categories available for TED talks in a multi-label classification task and (2) to determine what types of features drive classification accuracy for each of the categories.
Automated Classification of Written Proficiency Levels on the CEFR-Scale through Complexity Contours and RNNs
The key to the present paper is the combined use of what we refer to as ‘complexity contours’, a series of measurements of indices of L2 proficiency obtained by a computational tool that implements a sliding window technique, and recurrent neural network (RNN) classifiers that adequately capture the sequential information in those contours.
FANG-COVID: A New Large-Scale Benchmark Dataset for Fake News Detection in German
As the world continues to fight the COVID-19 pandemic, it is simultaneously fighting an ‘infodemic’ – a flood of disinformation and spread of conspiracy theories leading to health threats and the division of society.
MANTIS at SMM4H’2022: Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress
This paper describes our submission to Social Media Mining for Health (SMM4H) 2022 Shared Task 8, aimed at detecting self-reported chronic stress on Twitter.
The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from Reddit Posts
In recent years, there has been increasing interest in the application of natural language processing and machine learning techniques to the detection of mental health conditions (MHC) based on social media data.
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders
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.
Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media
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 and machine learning techniques.
(Psycho-)Linguistic Features Meet Transformer Models for Improved Explainable and Controllable Text Simplification
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 blackbox.
Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features
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 various text sources, including social media posts, micro-blogs or news articles.
Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features
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
There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading.
Prediction of Listener Perception of Argumentative Speech in a Crowdsourced Dataset Using (Psycho-)Linguistic and Fluency Features
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models
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 (Alzheimer's Dementia Recognition through Spontaneous Speech) challenge.
Becoming Linguistically Mature: Modeling English and German Children's Writing Development Across School Grades
In this paper we employ a novel approach to advancing our understanding of the development of writing in English and German children across school grades using classification tasks.
Understanding the Dynamics of Second Language Writing through Keystroke Logging and Complexity Contours
The purpose of this paper is twofold: [1] to introduce, to our knowledge, the largest available resource of keystroke logging (KSL) data generated by Etherpad (https://etherpad. org/), an open-source, web-based collaborative real-time editor, that captures the dynamics of second language (L2) production and [2] to relate the behavioral data from KSL to indices of syntactic and lexical complexity of the texts produced obtained from a tool that implements a sliding window approach capturing the progression of complexity within a text.
CoCoGen - Complexity Contour Generator: Automatic Assessment of Linguistic Complexity Using a Sliding-Window Technique
We present a novel approach to the automatic assessment of text complexity based on a sliding-window technique that tracks the distribution of complexity within a text.
