@article{MCAF0E5C4,
title = "A Transfer Learning Method for Solving Imbalance Data of Abusive Sentence Classification",
journal = "Journal of KIISE, JOK",
year = "2017",
issn = "2383-630X",
doi = "10.5626/JOK.2017.44.12.1275",
author = "Suin Seo,Sung-Bae Cho",
keywords = "natural language classification,class imbalance problem,transfer learning,characterlevel convolution neural network",
abstract = "The supervised learning approach is suitable for classification of insulting sentences, but pre-decided training sentences are necessary. Since a Character-level Convolution Neural Network is robust for each character, so is appropriate for classifying abusive sentences, however, has a drawback that demanding a lot of training sentences. In this paper, we propose transfer learning method that reusing the trained filters in the real classification process after the filters get the characteristics of offensive words by generated abusive/normal pair of sentences. We got higher performances of the classifier by decreasing the effects of data shortage and class imbalance. We executed experiments and evaluations for three datasets and got higher F1-score of character-level CNN classifier when applying transfer learning in all datasets."
}