title: ÐÐµÑÐ¾Ð´Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð² Ð·Ð°Ð´Ð°ÑÐ°Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÑÐµÐºÑÑÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Classification methods in the tasks of automated word processing
Creators: name: Ð ÑÐ´Ð¾Ð²ÑÐºÐ°Ñ, Ð¡Ð¾ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ·ÑÐºÐ¸
ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐÐ°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ°
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
natural language processing
classification

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð°Ð½Ð°Ð»Ð¸Ð·Ñ ÑÐµÐºÑÑÐ¾Ð²ÑÑ Ð·Ð°Ð¿Ð¸ÑÐµÐ¹ Ð¿ÐµÑÐ²Ð¸ÑÐ½ÑÑ Ð¾ÑÐ¼Ð¾ÑÑÐ¾Ð² Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ¾Ð² Ð¿ÑÐ¸ÑÐ¸Ð°ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÐºÐ»Ð¸Ð½Ð¸ÐºÐ¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ° Ñ Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸ÐµÐ¹ Ð½Ð° ÑÐ·ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ R. 2. ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐµÐºÑÑÐ¾Ð²ÑÑ Ð·Ð°ÐºÐ»ÑÑÐµÐ½Ð¸Ð¹ Ð¿ÑÐ¸ÑÐ¸Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¾ÑÐ¼Ð¾ÑÑÐ¾Ð² Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ¾Ð² Ð² ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²Ð¸Ð¸ Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾Ð·Ð°Ð¼Ð¸. 3. ÐÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð±Ð»Ð¸Ð·Ð¾ÑÑÐ¸ ÑÐµÐºÑÑÐ¾Ð². 4. Ð¡ÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸ Ð¾ÑÐµÐ½ÐºÐ° Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÑÐ»Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ñ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ Ð¼ÐµÑÐ¾Ð´Ñ Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð±Ð»Ð¸Ð·Ð¾ÑÑÐ¸ ÑÐµÐºÑÑÐ¾Ð²: TF-IDF Ð²ÐµÐºÑÐ¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ ÑÐµÐºÑÑÐ¾Ð² Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸ÐµÐ¼ ÐºÐ¾ÑÐ¸Ð½ÑÑÐ½Ð¾Ð¹ Ð±Ð»Ð¸Ð·Ð¾ÑÑÐ¸, ÑÐºÑÑÑÑÐ¹ ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸ÐµÐ¼ ÐºÐ¾ÑÐ¸Ð½ÑÑÐ½Ð¾Ð¹ Ð±Ð»Ð¸Ð·Ð¾ÑÑÐ¸, ÑÐºÑÑÑÐ¾Ðµ ÑÐ°Ð·Ð¼ÐµÑÐµÐ½Ð¸Ðµ ÐÐ¸ÑÐ¸ÑÐ»Ðµ Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸ÐµÐ¼ ÑÐ°ÑÑÐ¾Ð¶Ð´ÐµÐ½Ð¸Ñ ÐÐ¶ÐµÐ½ÑÐµÐ½Ð°-Ð¨ÐµÐ½Ð½Ð¾Ð½Ð°. ÐÑÐ»Ð° Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐµÐºÑÑÐ¾Ð² Ð¿ÐµÑÐ²Ð¸ÑÐ½ÑÑ Ð¾ÑÐ¼Ð¾ÑÑÐ¾Ð² Ð² ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²Ð¸Ð¸ Ñ ÐºÐ¾Ð´Ð°Ð¼Ð¸ Ð¼ÐµÐ¶Ð´ÑÐ½Ð°ÑÐ¾Ð´Ð½Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð±Ð¾Ð»ÐµÐ·Ð½ÐµÐ¹. ÐÑÐ¾Ð³Ð¾Ð²ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ñ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ð¼Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°Ð¼Ð¸: Ð¼ÐµÑÐ¾Ð´ Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð² Ñ ÑÐ°Ð´Ð¸Ð°Ð»ÑÐ½ÑÐ¼ ÑÐ´ÑÐ¾Ð¼, Ð¼ÐµÑÐ¾Ð´ Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½Ð¾Ð³Ð¾ Ð±ÑÑÑÐ¸Ð½Ð³Ð°, Ð¼ÐµÑÐ¾Ð´ ÑÐ»ÑÑÐ°Ð¹Ð½Ð¾Ð³Ð¾ Ð»ÐµÑÐ°.
description: The given work is devoted to the initial medical checkup texts analysis in a psychiatric clinic using data mining methods. The research set the following goals: 1. Studying the features of natural language processing with practical implementation in programming language R. 2. Initial medical checkup texts classification in accordance with diagnoses. 3. Determination of the similarity of texts. 4. Comparison of methods and evaluation of machine learning models. The following methods were used to determine the similarity of texts: TF-IDF vectorization with the calculation of cosine similarity, latent semantic analysis with the calculation of cosine similarity, latent Dirichlet allocation with the calculation of the Jensen-Shannon divergence. A texts classification of Initial medical checkup was carried out in accordance with the codes of the international classification of diseases, tenth revision. The resulting models are built with the following algorithms: support vector machine with a radial kernel, gradient boosting method, and random forest method.