Search Results for author:
Found 8 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.
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.
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.
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).
Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection
We study the selection of transfer languages for automatic abusive language detection.
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.
