Does Annotating Multi-Spans Improve Classification in Considerable Text Collections?

The Universal data markup structures empower the annotation of multiple text fragments (multi-spans or multi-fragments furthermore) to analyze large collections of content. Multi-spans have demonstrated helpful in tackling issues related to the programmed discovery of semantic blunders in school essays or human values in social media writings. Labeling multi-fragment information has made it conceivable to form an interdisciplinary classification of human values. This classifier consists of 105 labels grouped into 7 categories, and a corresponding dataset has been created. Subsequent ML experiments have been designed to demonstrate the effectiveness of the multi-spans structure in recovering annotations of human values. The accuracy of the multi-fragment detector is 0.943 for material values and 0.957 for legal awareness (a subject of civic engagement and citizenship).