SEMANTIC-AND-SYNTACTIC PARSING OF SENTENCES IN RUSSIAN USING NEURAL NEWTROKS
Currently, there are many software systems using input data presented as texts and sentences in natural language. The complex, ambiguous and fluent syntax of Russian language causes very difficult implementation of automatic semantic-and-syntactic parser for sentences in Russian. All existing solutions have a common drawback: the output quality strongly depends on the completeness of an ontology base. However, it is impossible to create a complete ontology base for Russian language. It has been suggested that the evaluation of numeric characteristics, calculated from word positions in a sentence without using any human-developed ontology base is sufficient to build a semantic-and-syntax tree. Preliminary, words must be marked with possible meanings and morphological characteristics. Evaluation is performed with machine-learning algorithms. A prototype was developed to determine the accordance of a word group to a node of a semantic-and-syntax tree. First two chapters of M.A. Bulgakov’s novel «Master and Margarita» have been particularly parsed using the prototype. The obtained results allow evaluate applicability of the described algorithm to solve real issues concerned with processing data presented as texts and sentences in Russian.