Some Investigations on Machine Learning Techniques for Automated Text Categorization

Abstract

The automated categorization (classification) of texts into predefined categories is one of the widely explored fields of research in text mining. Now-a-days, availability of digital data is very high, and to manage them in predefined categories has become a challenging task. Machine learning technique is an approach by which we can train automated classifier to classify the documents with minimum human assistance. This paper discusses the Naïve Bayes, Rocchio, k-Nearest Neighborhood and Support Vector Machine methods within machine learning paradigm for automated text categorization of given documents in predefined categories.

Fine-Tuning Transformer Models Using Transfer Learning for Multilingual Threatening Text Identification

“…The first approach enforces the design of a separate classification framework for each language [7,8]. The second approach employs the step of translation for all languages into one universal language before applying a single classification system [9,10]. The third approach emphasizes the development of one classification framework for different languages.…”