Natural language processing and word translation

This is what a similar project looks like for a large oil company. The customer was faced with the task of preparing data on assets: industrial installations, equipment in operation, as well as measurement and control instruments. The sources of the data were text documents - technical regulations that most fully describe the technical processes and the necessary production facilities.

We have demonstrated the possibility of using ML and NLP technologies to extract information from a textual description (and generate equipment profiles based on it). The generated profiles were compared with the results of manual mapping, taken as a standard - the achieved accuracy was 97.3%. The approach allows you to significantly reduce labor and time costs, as well as minimize the risks associated with errors in manual word processing.

How is natural language processed?

Some NLP problems for natural language, as opposed to image processing, have until recently been solved using classical machine learning algorithms.

Most of the tasks (like doctranslator) required a careful choice of architecture, as well as manual collection and processing of features. Recently, however, neural networks have begun to give more accurate results compared to classical models and have formed a general approach for solving NLP problems.