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Found 16 papers, 0 papers with code
Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art
Based on recognizing those research gaps, we provide some suggestions for improving the general research conduct in cyberbullying detection, with a primary focus on low-resource languages.
Vulgar Remarks Detection in Chittagonian Dialect of Bangla
The negative effects of online bullying and harassment are increasing with Internet popularity, especially in social media.
Improving Polish to English Neural Machine Translation with Transfer Learning: Effects of Data Volume and Language Similarity
This paper investigates the impact of data volume and the use of similar languages on transfer learning in a machine translation task.
Zero-shot cross-lingual transfer language selection using linguistic similarity
This allows us to select a more suitable transfer language which can be used to better leverage knowledge from high-resource languages in order to improve the performance of language applications lacking data.
Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining
Furthermore, we find that if a model pretrained on a related speech variety or an unrelated language with similar phonological characteristics is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance when there is very little labeled data in the target language.
Can You Fool AI by Doing a 180? $\unicode{x2013}$ A Case Study on Authorship Analysis of Texts by Arata Osada
Firstly, since the methods used for performing authorship analysis imply that an author can be recognized by the content he or she creates, we were interested in finding out whether it would be possible for an author identification system to correctly attribute works to authors if in the course of years they have undergone a major psychological transition.
Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection
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 specific focus on linguistically-backed embeddings in Convolutional Neural Networks (CNN).
Comparing Performance of Different Linguistically-Backed Word Embeddings for Cyberbullying Detection
In most cases, word embeddings are learned only from raw tokens or in some cases, lemmas.
Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection
We study the selection of transfer languages for automatic abusive language detection.
In the Service of Online Order: Tackling Cyber-Bullying with Machine Learning and Affect Analysis
Firstly, we analysed the entries with a multifaceted affect analysis system in order to find distinctive features for cyber-bullying and apply them to a machine learning classifier.
Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density
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 used to comparatively estimate the potential performance of machine learning (ML) classifiers prior to any training.
Epistolary Education in 21st Century: A System to Support Composition of E-mails by Students to Superiors in Japanese
E-mail is a communication tool widely used by people of all ages on the Internet today, often in business and formal situations, especially in Japan.
