155 citations · 364 across the 11 of their papers we have counts for
11 papers
Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art
Tanjim Mahmud, Michal Ptaszynski, Juuso Eronen +1
The struggle of social media platforms to moderate content in a timely manner, encourages users to abuse such platforms to spread vulgar or abusive language, which, when performed…
Improving Polish to English Neural Machine Translation with Transfer Learning: Effects of Data Volume and Language Similarity
Juuso Eronen, Michal Ptaszynski, Karol Nowakowski +2
This paper investigates the impact of data volume and the use of similar languages on transfer learning in a machine translation task. We find out that having more data generally l…
Zero-shot cross-lingual transfer language selection using linguistic similarity
Juuso Eronen, Michal Ptaszynski, Fumito Masui
We study the selection of transfer languages for different Natural Language Processing tasks, specifically sentiment analysis, named entity recognition and dependency parsing. In o…
Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining
Karol Nowakowski, Michal Ptaszynski, Kyoko Murasaki +1
In recent years, neural models learned through self-supervised pretraining on large scale multilingual text or speech data have exhibited promising results for underresourced langu…
Can You Fool AI by Doing a 180? $\unicode{x2013}$ A Case Study on Authorship Analysis of Texts by Arata Osada
Jagna Nieuwazny, Karol Nowakowski, Michal Ptaszynski +1
This paper is our attempt at answering a twofold question covering the areas of ethics and authorship analysis. Firstly, since the methods used for performing authorship analysis i…
Exploring the Potential of Feature Density in Estimating Machine Learning Classifier Performance with Application to Cyberbullying Detection
Juuso Eronen, Michal Ptaszynski, Fumito Masui +2
In this research. we analyze the potential of Feature Density (HD) as a way to comparatively estimate machine learning (ML) classifier performance prior to training. The goal of th…
Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection
Juuso Eronen, Michal Ptaszynski, Fumito Masui +4
In this research, we study the change in the performance of machine learning (ML) classifiers when various linguistic preprocessing methods of a dataset were used, with the specifi…
Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection
Juuso Eronen, Michal Ptaszynski, Fumito Masui +3
We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-…
In the Service of Online Order: Tackling Cyber-Bullying with Machine Learning and Affect Analysis
Michal Ptaszynski, Pawel Dybala, Tatsuaki Matsuba +4
One of the burning problems lately in Japan has been cyber-bullying, or slandering and bullying people online. The problem has been especially noticed on unofficial Web sites of Ja…
Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density
Juuso Eronen, Michal Ptaszynski, Fumito Masui +3
We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods in order to estimate dataset complexity, which in turn is use…
Cyberbullying Detection -- Technical Report 2/2018, Department of Computer Science AGH, University of Science and Technology
Michał Ptaszyński, Gniewosz Leliwa, Mateusz Piech +1
The research described in this paper concerns automatic cyberbullying detection in social media. There are two goals to achieve: building a gold standard cyberbullying detection da…