Nowadays most of people of developed economy countries interact with software every day. As a result of computer systems expansion to all scope of people's activity the problem of transition from visual and command interfaces to natural language user interfaces is thrown into the sharp relief. Computational linguistics and natural language processing methods are described in this article. Methods of natural language manipulation are applied in machine translation software systems, systems of search and exchange the data, text annotation and expert systems. Prototype of natural language user interface to structured data source is developed. As a result it is convert natural language user's query to SQL query to database. Natural language user interface is created to predefined subject field. User interface interacts to database that contain information about existent program libraries and frameworks. Consequently, using natural language processing methods it is possible to develop user natural language user interface providing capability to interact with machine.

Results of a study of semantic networks from the viewpoint of the practical usage for knowledge visualization and retrieval. The capabilities of graphs with a verb at the center together with relation

Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based on the Bell-like test, which is well-known in quantum physics. We propose quantum probability theory in the hyperspace analog to language (HAL) framework exploiting a Hilbert space for word and document vector specification. The quantum approach allows for accounting for specific user preferences in different contexts. To verify the algorithm proposed, we use a dataset of synthetic advertising text documents from travel agencies generated by the OpenAI GPT-4 model. We show that the “entanglement” in two-word document search and retrieval can be recognized as the frequent occurrence of two words in incompatible query contexts. We have found that the user preferences and word ordering in the query play a significant role in relatively small sizes of the HAL window. The comparison with the cosine similarity metrics demonstrates the key advantages of our approach based on the user-enforced contextual and semantic relationships between words and not just their superficial occurrence in texts. Our approach to retrieving and ranking documents allows for the creation of new information search engines that require no resource-intensive deep machine learning algorithms.

Syntactic text analysis is a very important step of automatic text processing. The key problem is that all existing approaches are dictionary-dependent. It can be impossible to analyze all sentences because of the lack of one word in the dictionary. However even the presence of the dictionary does not resolve the phrases interpretation ambiguity. At the same time fusional languages contain enough information about the parts of speech. Particularly, the endings of nouns, adjectives, verbs and participles cannot fully indicate their case, number and face because of semantic ambiguity. Prepositions also cannot always correctly indicate the cases. This yields a variety phrase interpretation. To solving this problem sharing affixes and prepositions is proposed. However fusional languages have some disadvantages. One of this is the allowance of building phrases with random word order. It leads to increase the number of options. The paper proposes to use sentences templates with analyzed phrases for ambiguity decrease. In addition usage of affixes with prepositions for text analysis provides the basic elements of the semantic analysis without a dictionary.