Semiotic analysis of narrative legal texts using asymmetric document divergency

Abstract

In the paper the case is studied then semiotic signs can be represented as language constructs in the same language as the text for the interpretation. The goal is to obtain estimates of the depth of interpretability with the respect to each of the signs by finding the projections of the narrative on these language constructs. That is, we find the probability of the presence of each of the signs. By use the embedding of linguistic constructions into a multidimensional numerical vector space and the grid layout, which is the image of the system of signs. At the stage of the inference, our model computes the nodes of the grid, which are the most adequate to the input text by the method of new proposed asymmetric similarity. The constructed model was studied using Russian criminal, civil and labour legislation. We suggest using the models called the constellation as for the grid of normative acts and the narrative together with the corresponding divergence. In this paper, exanimated the pre-trained models known in NLP as doc2vec and Fast Text. The evaluation of the quality of models was carried out using open databases of court decisions.