Encoding semantic relationships in literary texts

Encoding meaningful semantic relationships in literary texts is almost as difficult defining and identifying them. Defining the types components of that can be extracted from a quite challenging task because literature full implicit oblique messages references. Subsequently, encoding even more often relations do not have neither clear nor standard linguistic form usually they overlap each other. This paper discusses modeling issues concerning mapping cultural content humanities texts, highlighted by case ECARLE project annotation campaign. On handling these proposes methodology minimalistic flexible techniques, combined order to generate human annotated training data for Relation Extraction machine learning system. The proposed utilizes available TEI tagset, and, without any further customizations, allows formed named entities simple yet way, open reuse, interchange, conversion visualization.