Identification of Semantic Patterns in Full-text Documents Using Neural Network Methods

Abstract

Processing and text mining are becoming increasingly possible thanks to the development of computer technology, as well as the development of artificial intelligence (machine learning). This article describes approaches to the analysis of texts in natural language using methods of morphological, syntactic and semantic analysis. Morphological and syntactic analysis of the text is carried out using the Pullenti system, which allows not only to normalize words, but also to distinguish named entities, their characteristics, and relationships between them. As a result, a semantic network of related named entities is built, such as people, positions, geographical names, business associations, documents, education, dates, etc. The word2vec technology is used to identify semantic patterns in the text based on the joint occurrence of terms. The possibility of joint use of the described technologies is being considered.