Research and development of linguo-statistical methods for forming a portrait of a subject area

Abstract

The project aims to solve the fundamental scientific problem of semantic modeling, within the framework of which a methodology is developed for the automated identification of translation links (translation correspondences), as well as hierarchical, synonymous and associative links from Internet texts and the construction of multilingual associative hierarchical portraits of subject area (MAHPSA), in particular, on autonomous uninhabited underwater vehicles (UUV). Accounting for multilingual and heterogeneous resources allows you to get a more complete picture of what is happening in the subject area, to identify the sources of the origin of ideas, the speed and directions of their distribution, to identify significant documents and promising directions. The solution to the problem is based on an integrated approach that combines the methods of statistics, corpus linguistics and distributive semantics, and is implemented in technology that involves the development of linguo-statistical mechanisms for the formation of a multilingual associative hierarchical portrait of a subject area, which is a dictionary of significant terms of the subject area, the elements of which organized in synonymous series (synsets), including translational correspondences, as well as associative and hierarchical relationships.

Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports

“…To extract the required entities, Named Entity Recognition (NER) algorithms from the PullEnti [29,30] and Natasha [31,32] Python libraries were employed. These algorithms were used to identify patterns associated with the specified entities effectively [33].…”