no code implementations • EACL (BEA) 2021 • Elma Kerz, Daniel Wiechmann, Yu Qiao, Emma Tseng, Marcus Ströbel
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.
no code implementations • RDSM (COLING) 2020 • Yu Qiao, Daniel Wiechmann, Elma Kerz
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.
no code implementations • EACL (WASSA) 2021 • Elma Kerz, Yu Qiao, Daniel Wiechmann
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.
1 code implementation • LREC 2022 • Elma Kerz, Yu Qiao, Sourabh Zanwar, Daniel Wiechmann
In recent years, there has been increasing interest in automatic personality detection based on language.
1 code implementation • EMNLP (FEVER) 2021 • Justus Mattern, Yu Qiao, Elma Kerz, Daniel Wiechmann, Markus Strohmaier
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.
no code implementations • SMM4H (COLING) 2022 • Sourabh Zanwar, Daniel Wiechmann, Yu Qiao, Elma Kerz
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.
no code implementations • SMM4H (COLING) 2022 • Sourabh Zanwar, Daniel Wiechmann, Yu Qiao, Elma Kerz
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.
no code implementations • 19 Dec 2022 • Sourabh Zanwar, Daniel Wiechmann, Yu Qiao, Elma Kerz
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.
no code implementations • 19 Dec 2022 • Sourabh Zanwar, Daniel Wiechmann, Yu Qiao, Elma Kerz
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.
no code implementations • 19 Dec 2022 • Xiaofei Li, Daniel Wiechmann, Yu Qiao, Elma Kerz
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.
no code implementations • 19 Dec 2022 • Yu Qiao, Xiaofei Li, Daniel Wiechmann, Elma Kerz
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.
no code implementations • WASSA (ACL) 2022 • Elma Kerz, Yu Qiao, Sourabh Zanwar, Daniel Wiechmann
Research at the intersection of personality psychology, computer science, and linguistics has recently focused increasingly on modeling and predicting personality from language use.
no code implementations • ACL 2022 • Daniel Wiechmann, Yu Qiao, Elma Kerz, Justus Mattern
There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading.
no code implementations • 13 Nov 2021 • Yu Qiao, Sourabh Zanwar, Rishab Bhattacharyya, Daniel Wiechmann, Wei Zhou, Elma Kerz, Ralf Schlüter
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) +1
no code implementations • 16 Jun 2021 • Yu Qiao, Xuefeng Yin, Daniel Wiechmann, Elma Kerz
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.
no code implementations • 17 Apr 2021 • Yu Qiao, Wei Zhou, Elma Kerz, Ralf Schlüter
In recent years, automated approaches to assessing linguistic complexity in second language (L2) writing have made significant progress in gauging learner performance, predicting human ratings of the quality of learner productions, and benchmarking L2 development.
no code implementations • WS 2020 • Elma Kerz, Yu Qiao, Daniel Wiechmann, Marcus Str{\"o}bel
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.
no code implementations • LREC 2020 • Elma Kerz, Fabio Pruneri, Daniel Wiechmann, Yu Qiao, Marcus Str{\"o}bel
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.
no code implementations • WS 2019 • Elma Kerz, Arndt Heilmann, Stella Neumann
A substantial body of research has demonstrated that native speakers are sensitive to the frequencies of multiword sequences (MWS).
no code implementations • WS 2016 • Str{\"o}bel Marcus, Elma Kerz, Daniel Wiechmann, Stella Neumann
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.