Processing and Analysis of Russian Strategic Planning Programs

P. 68–81.

Алексейчук Н. Н., Sarkisyan V., Emelyanov A., Artemova E.

In this paper, we present a project on the analysis of an extensive corpus of strategic planning documents, devoted to various aspects of the development of Russian regions. The main purposes of the project are: 1) to extract different aspects of goal setting and planning, 2) to form an ontology of goals and criteria of achieving these goals, 3) to measure the similarity between goals declared by federal and municipal subjects. 

Such unsupervised Natural Language Processing (NLP) methods as phrase chunking,  word embeddings, and latent topic modeling are used for information extraction and ontology construction as well as similarity computation. 
The resulting ontology should serve in short-term as a helper tool for writing strategic planning documents and in long-term resolve the need to compose strategic planning documents completely by navigating through the ontology and selecting relevant goals and criteria. The resulting similarity measure between federal and municipal goals will serve as a navigation tool for further analysis.  

Keywords: анализ текстов, text mining, probabilistic topic model, вероятностное тематическое моделирование

Publication based on the results of: Development of Mathematical Models and Methods for Recommender Systems and Natural Language Processing (2019)

In book

Digital Transformation and Global Society. Fourth International Conference, DTGS 2019, St. Petersburg, Russia, June 19–21, 2019, Revised Selected Papers

Springer, 2019.