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Analysis of methods and tools for image colorization was performed. It was explained why artificial neural network model was chosen for graphics information processing. The task of automatic colorization of arbitrary images was formulated. Initial data, conditions and constraints necessary for colorization model are listed. As a result of text classification, set of neural network hypercolumns was retrieved for each image processed. Colorization model was created which allows to determine color of each pixel based on hypercolumns set. In fact, this model consists of two related parts: classifier and colorizer. Classifier is based on using convolutional neural network, and colorizer is based on hash table which stores mapping of hypercolumns and colors. Algorythm of using this model for image colorization is proposed. Comparison of colorization results for developed and existing models was performed. Software tool was created which allows to perform learning of different neural networks and colorization of graphical information. Experiments shown that developed model determines image color quite correctly. Proposed algorithm allows to use convolutional neural network for colorizing black-and-white images, for color correction of pictures, etc.

Semantic network model for representing data and knowledge was analysed. Selection of this model for working with text information was justified. The objective of automatic semantic network generation based on an arbitrary Russian-language text was formulated. Initial data, conditions and constraints necessary for network generation algorithm are listed. As a result of the part-of-speech analysis for each word and word order in a sentence, semantic relations between words are determined. The Lexeme dictionary was created to determine the part of speech of words in sentences. A set of question types used in the semantic network was selected. The number of relations in the network is regulated due to the possibility to use only necessary relation types when resolving a specific task. With that, the relations in semantic network can have very different types, which makes it a universal model for representing data and knowledge. The algorithm was developed which allows one to get answers for the questions asked. The semantic network model was generated automatically for the sentences considered. In the proposed algorithm the semantic network is interpreted as unoriented graph on which breadth-first search algorithm is used to find an answer. The proposed algorithms were implemented in a software tool which automatically generates the semantic network for an arbitrary text. The created software tool allows asking questions and getting answers to them based on the information which is stored in the semantic network. The experiments have shown that the generated semantic network gives correct answers to the questions posed. The network is modified by adding and removing information in it. There is a possibility to choose complexity of network structure depending on a specific task being resolved. The proposed approach for building and working with the semantic network allows one to process texts in various languages, to use it in information systems with natural-language interface, and to resolve such tasks as text classification and text search.