Text Augmentation Techniques for Document Vector Generation from Russian News Articles

Abstract

In this paper, a document classification system is enhanced through the construction of a text augmentation technique by testing various Part-of-Speech filters and word vector weighting methods with nine different models for document representation. Subject/object tagging is introduced as a new form of text augmentation, along with a novel classification system grounded in a word weighting method based on the distribution of words among classes of documents. When an augmentation including subject/object tagging, a nouns+adjectives filter and Inverse Document Frequency word weighting was applied, an average increase in classification accuracy of 4.1% points was observed.