# Hybridization of Intelligent Solutions Architecture for Text Understanding and Text Generation
## 2 State-of-the-Art  
The problem of automated understanding of text, as a separate domain of information analysis, emerged quite a long time ago, however, it was finally formed into a separate scientific direction by the 1980s [1]. Nowadays, intelligent text understanding is implemented to solve various problems [2]. For example, text analysis algorithms are used to monitor social media in order to identify and prevent various events [3; 4]. Basic features of the implementation of the morphological analysis of informal texts on the Internet are considered in [5]. Data processing in natural language is also used in the problem of machine translation of texts or speech without human intervention, using the construction of deep neural networks [6]. The main challenge is concerned with understanding the semantics of the text [7]. To correctly determine the meaning of each passage, the reader and author must have a similar context formed by the background knowledge. Understanding the context is necessary, on the one hand, to improve the quality of text analysis, and on the other hand, to extract implicit information. Early approaches addressed this problem by defining a kind of knowledge structure, building a plan or scenario. However, later, general classes of inference were identified that do not depend on individual knowledge structures [8], which provided a transition to patterns for establishing connections between the concepts of the context. The classical approach to text understanding uses a semantic analyzer and two static semantic resources: combinatorial vocabulary and ontology [9]. The vocabulary contains a variety of information about words, and the ontology stores non-linguistic knowledge about concepts and serves as a meta-language for semantic description. Words and concepts are provided with semantic descriptions, which consist of definitions in a formal language. In general, legal documentation analysis is one of the perspective areas of text understanding applications. Solutions based on the use of ontologies containing knowledge about the subject area are actively used to detect and eliminate errors in the consistency of the extracted data, including legal documents [10]. In the context of legal documentation processing, the automatic search for relevant legal provisions in the written description of events is a challenging problem [11]. The method for classifying legal texts is based on characteristic word analysis. To organize the connection between legal provisions and characteristic words, court decisions are taken as a training set of documents, since the corresponding provisions can be accurately extracted from them. The corresponding relationship is then established using the TF-IDF algorithm. Experiments show that the algorithm can extract keywords from various legal texts and classify them into the corresponding legal terms. A method for automatic classification of scientific texts [12] is based on data compression. Experiments have shown that the method correctly identifies topics of scientific texts with a probability of 75-95%. The transition to patterns and knowledge bases in logical inference made it possible to use artificial intelligence technologies, e.g., neural networks. When using them, the mechanism of \"attention\" is highlighted, being a key component at the stage of training a neural network. The attention engine models how important each part of the original sentence is. To compute importance scores, the attention engine summarizes the information and then creates a context vector. Experimental results in [13] show that paying attention to words significantly improves the quality of the final solution. Text features should also be considered. Short passages are not easy to process with conventional methods due to incorrect or abbreviated syntax and the lack of statistical signals [14]. Concept dependencies can be used to find the hidden semantics of short texts.