Data Scarcity: Methods to Improve the Quality of Text Classification

Abstract

Legal document analysis is an important research area. The classification of clauses or sentences enables valuable insights such as the extraction of rights and obligations. However, datasets consisting of contracts or other legal documents are quite rare, particularly regarding the German language. The exorbitant cost of manually labeled data, especially in regard to text classification, is the motivation of many studies that suggest alternative methods to overcome the lack of labeled data. This paper experiments the effects of text data augmentation on the quality of classification tasks. While a large amount of techniques exists, this work examines a selected subset including semi-supervised learning methods and thesaurus-based data augmentation. We could not just show that thesaurus-based data augmentation as well as text augmentation with synonyms and hypernyms can improve the classification results, but also that the effect of such methods depends on the underlying data structure.