{"id":8104,"date":"2020-07-01T09:58:10","date_gmt":"2020-07-01T06:58:10","guid":{"rendered":"https:\/\/ssrlab.by\/?p=8104"},"modified":"2020-12-15T12:50:41","modified_gmt":"2020-12-15T09:50:41","slug":"poshuk-najblizhejshyh-slo%d1%9e","status":"publish","type":"post","link":"https:\/\/ssrlab.by\/en\/8104","title":{"rendered":"Nearest Words Finder"},"content":{"rendered":"<p>Nearest Words Finder\"><img loading=\"lazy\" class=\"alignnone size-full wp-image-8105\" src=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-1.png\" alt=\"\" width=\"1200\" height=\"432\" srcset=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-1.png 1200w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-1-300x108.png 300w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-1-1024x369.png 1024w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-1-768x276.png 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><\/span><\/p>\n<p align=\"center\"><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041c\u0430\u043b\u044e\u043d\u0430\u043a 1 \u2013 \u0413\u0440\u0430\u0444\u0456\u0447\u043d\u044b \u0456\u043d\u0442\u044d\u0440\u0444\u0435\u0439\u0441 \u0441\u044d\u0440\u0432\u0456\u0441\u0430 \u00ab\u041f\u043e\u0448\u0443\u043a \u043d\u0430\u0439\u0431\u043b\u0456\u0436\u044d\u0439\u0448\u044b\u0445 \u0441\u043b\u043e\u045e\u00bb<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0406\u043d\u0442\u044d\u0440\u0444\u0435\u0439\u0441 \u043c\u0430\u0435 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u044b\u044f \u0432\u043e\u0431\u043b\u0430\u0441\u0446\u0456:<\/span><\/span><\/p>\n<ul>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041f\u043e\u043b\u0435 \u045e\u0432\u043e\u0434\u0443 \u0442\u044d\u043a\u0441\u0442\u0443 \u0434\u043b\u044f \u0430\u043f\u0440\u0430\u0446\u043e\u045e\u043a\u0456. \u0414\u0430\u0434\u0437\u0435\u043d\u0430\u0435 \u043f\u043e\u043b\u0435 \u0437\u0430\u0431\u044f\u0441\u043f\u0435\u0447\u0430\u043d\u0430\u0435 \u043a\u043d\u043e\u043f\u043a\u0430\u043c\u0456 \u00ab\u0410\u0431\u043d\u0430\u0432\u0456\u0446\u044c\u00bb (\u0432\u044f\u0440\u043d\u0443\u0446\u044c \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u044f \u043f\u0430 \u0437\u043c\u0430\u045e\u0447\u0430\u043d\u043d\u0456) \u0456 \u00ab\u0410\u0447\u044b\u0441\u0446\u0456\u0446\u044c\u00bb (\u0432\u044b\u0434\u0430\u043b\u0456\u0446\u044c \u0443\u0441\u0435 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u044f);<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041f\u043e\u043b\u0435 \u00ab\u0421\u043b\u043e\u045e\u043d\u0456\u043a\u00bb \u0434\u043b\u044f \u045e\u0432\u043e\u0434\u0443 \u0441\u043b\u043e\u045e \u0434\u043b\u044f \u043f\u0440\u0430\u0432\u0435\u0440\u043a\u0456. \u0414\u0430\u0434\u0437\u0435\u043d\u0430\u0435 \u043f\u043e\u043b\u0435 \u0442\u0430\u043a\u0441\u0430\u043c\u0430 \u0437\u0430\u0431\u044f\u0441\u043f\u0435\u0447\u0430\u043d\u0430\u0435 \u043a\u043d\u043e\u043f\u043a\u0430\u043c\u0456 \u00ab\u0410\u0431\u043d\u0430\u0432\u0456\u0446\u044c\u00bb \u0456 \u00ab\u0410\u0447\u044b\u0441\u0446\u0456\u0446\u044c\u00bb;<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041e\u043f\u0446\u044b\u044f \u00ab\u041d\u0435 \u0430\u0434\u043b\u044e\u0441\u0442\u0440\u043e\u045e\u0432\u0430\u0446\u044c \u0441\u043b\u043e\u0432\u044b, \u0437\u043d\u043e\u0439\u0434\u0437\u0435\u043d\u044b\u044f \u045e \u0441\u043b\u043e\u045e\u043d\u0456\u043a\u0443\u00bb, \u044f\u043a\u0430\u044f \u045e\u043f\u043b\u044b\u0432\u0430\u0435 \u043d\u0430 \u043a\u0430\u043d\u0447\u0430\u0442\u043a\u043e\u0432\u044b \u0432\u044b\u043d\u0456\u043a \u043f\u0440\u0430\u0446\u044b \u0441\u044d\u0440\u0432\u0456\u0441\u0430;<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041a\u043d\u043e\u043f\u043a\u0430 \u00ab\u0410\u043f\u0440\u0430\u0446\u0430\u0432\u0430\u0446\u044c!\u00bb, \u044f\u043a\u0430\u044f \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435 \u0430\u043f\u0440\u0430\u0446\u043e\u045e\u043a\u0443 \u0456 \u0434\u0430\u0435 \u043c\u0430\u0433\u0447\u044b\u043c\u0430\u0441\u0446\u044c \u0430\u0442\u0440\u044b\u043c\u0430\u0446\u044c \u0432\u044b\u043d\u0456\u043a\u0456.<\/span><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041f\u0430\u0441\u043b\u044f \u043d\u0430\u0446\u0456\u0441\u043a\u0430\u043d\u043d\u044f \u043d\u0430 \u043a\u043d\u043e\u043f\u043a\u0443 \u00ab\u0410\u043f\u0440\u0430\u0446\u0430\u0432\u0430\u0446\u044c!\u00bb \u0456 \u043f\u0440\u0430\u0432\u044f\u0434\u0437\u0435\u043d\u043d\u044f \u0430\u043f\u0440\u0430\u0446\u043e\u045e\u043a\u0456 \u045e\u043d\u0456\u0437\u0435 \u044d\u043a\u0440\u0430\u043d\u0430 \u0437\u2019\u044f\u045e\u043b\u044f\u0435\u0446\u0446\u0430 \u0442\u0430\u0431\u043b\u0456\u0446\u0430 \u0437 \u0432\u044b\u043d\u0456\u043a\u0430\u043c\u0456.<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\"><b>\u041a\u0430\u0440\u044b\u0441\u0442\u0430\u043b\u044c\u043d\u0456\u0446\u043a\u0456\u044f \u0441\u0446\u044d\u043d\u0430\u0440\u044b\u0456 \u043f\u0440\u0430\u0446\u044b \u0437 \u0441\u044d\u0440\u0432\u0456\u0441\u0430\u043c<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\"><u>\u0421\u0446\u044d\u043d\u0430\u0440\u044b\u0439 1. \u0410\u043f\u0440\u0430\u0446\u043e\u045e\u043a\u0430 \u0437 \u0430\u0434\u043b\u044e\u0441\u0442\u0440\u0430\u0432\u0430\u043d\u043d\u0435\u043c \u0443\u0441\u0456\u0445 \u0441\u043b\u043e\u045e \u0437\u044b\u0445\u043e\u0434\u043d\u0430\u0433\u0430 \u0442\u044d\u043a\u0441\u0442\u0443<\/u><\/span><\/span><\/p>\n<ol>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0423\u0432\u0435\u0441\u0446\u0456 \u0442\u044d\u043a\u0441\u0442 \u0443 \u043f\u043e\u043b\u0435 \u045e\u0432\u043e\u0434\u0443 \u0442\u044d\u043a\u0441\u0442\u0443.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0423\u0432\u0435\u0441\u0446\u0456 \u0441\u043b\u043e\u0432\u044b \u0434\u043b\u044f \u043f\u0440\u0430\u0432\u0435\u0440\u043a\u0456, \u0440\u0430\u0437\u0434\u0437\u0435\u043b\u0435\u043d\u044b\u044f \u043f\u0440\u0430\u0431\u0435\u043b\u0430\u043c\u0456 \u0456\/\u0430\u0431\u043e \u0441\u0456\u043c\u0432\u0430\u043b\u0430\u043c\u0456 \u0442\u0430\u0431\u0443\u043b\u044f\u0446\u044b\u0456 \u0456 \u043f\u0435\u0440\u0430\u0432\u043e\u0434\u0443 \u0440\u0430\u0434\u043a\u0430, \u0443 \u043f\u043e\u043b\u0435 \u00ab\u0421\u043b\u043e\u045e\u043d\u0456\u043a\u00bb.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041f\u0435\u0440\u0430\u043a\u0430\u043d\u0430\u0446\u0446\u0430, \u0448\u0442\u043e \u043e\u043f\u0446\u044b\u044f \u00ab\u041d\u0435 \u0430\u0434\u043b\u044e\u0441\u0442\u0440\u043e\u045e\u0432\u0430\u0446\u044c \u0441\u043b\u043e\u0432\u044b, \u0437\u043d\u043e\u0439\u0434\u0437\u0435\u043d\u044b\u044f \u045e \u0441\u043b\u043e\u045e\u043d\u0456\u043a\u0443\u00bb \u0430\u0434\u043a\u043b\u044e\u0447\u0430\u043d\u0430\u044f. \u041a\u0430\u043b\u0456 \u043e\u043f\u0446\u044b\u044f \u0443\u043a\u043b\u044e\u0447\u0430\u043d\u0430, \u0437\u043d\u044f\u0446\u044c \u0430\u0434\u043f\u0430\u0432\u0435\u0434\u043d\u044b \u0441\u0446\u044f\u0436\u043e\u043a.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041d\u0430\u0446\u0456\u0441\u043d\u0443\u0446\u044c \u043a\u043d\u043e\u043f\u043a\u0443 \u00ab\u0410\u043f\u0440\u0430\u0446\u0430\u0432\u0430\u0446\u044c!\u00bb.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0410\u0442\u0440\u044b\u043c\u0430\u0446\u044c \u0432\u044b\u043d\u0456\u043a \u0443 \u043f\u043e\u043b\u0456 \u0432\u044b\u0434\u0430\u0447\u044b \u0432\u044b\u043d\u0456\u043a\u0430\u045e. \u041f\u0440\u044b \u043d\u0435\u0430\u0431\u0445\u043e\u0434\u043d\u0430\u0441\u0446\u0456 \u045e\u043d\u0435\u0441\u0446\u0456 \u0437\u043c\u0435\u043d\u044b \u045e \u0437\u044b\u0445\u043e\u0434\u043d\u044b \u0442\u044d\u043a\u0441\u0442 \u0456 \u043d\u0430\u0446\u0456\u0441\u043d\u0443\u0446\u044c \u043a\u043d\u043e\u043f\u043a\u0443 \u00ab\u0410\u043f\u0440\u0430\u0446\u0430\u0432\u0430\u0446\u044c!\u00bb \u044f\u0448\u0447\u044d \u0440\u0430\u0437.<\/span><\/span><\/li>\n<\/ol>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041c\u0430\u0433\u0447\u044b\u043c\u044b \u0432\u044b\u043d\u0456\u043a \u043f\u0440\u0430\u0446\u044b \u0441\u044d\u0440\u0432\u0456\u0441\u0430 \u0437\u0433\u043e\u0434\u043d\u0430 \u0437 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u043c \u0441\u0446\u044d\u043d\u0430\u0440\u044b\u0435\u043c \u043f\u0440\u0430\u0434\u0441\u0442\u0430\u045e\u043b\u0435\u043d\u044b \u043d\u0430 \u043c\u0430\u043b\u044e\u043d\u043a\u0443 2.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><a href=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-2.png\" class=\"grouped_elements\" rel=\"tc-fancybox-group8104\" title=\"Nearest Words Finder\"><img loading=\"lazy\" class=\"alignnone size-full wp-image-8106\" src=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-2.png\" alt=\"\" width=\"1192\" height=\"296\" srcset=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-2.png 1192w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-2-300x74.png 300w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-2-1024x254.png 1024w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-2-768x191.png 768w\" sizes=\"(max-width: 1192px) 100vw, 1192px\" \/><\/a><\/span><\/p>\n<p align=\"center\"><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041c\u0430\u043b\u044e\u043d\u0430\u043a 2 \u2013 \u0412\u044b\u043d\u0456\u043a \u043f\u0440\u0430\u0446\u044b \u0441\u044d\u0440\u0432\u0456\u0441\u0430 \u00ab\u041f\u043e\u0448\u0443\u043a \u043d\u0430\u0439\u0431\u043b\u0456\u0436\u044d\u0439\u0448\u044b\u0445 \u0441\u043b\u043e\u045e\u00bb \u0437\u0433\u043e\u0434\u043d\u0430 \u0441\u0430 \u0441\u0446\u044d\u043d\u0430\u0440\u044b\u0435\u043c 1<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\"><u>\u0421\u0446\u044d\u043d\u0430\u0440\u044b\u0439 2. \u0410\u043f\u0440\u0430\u0446\u043e\u045e\u043a\u0430 \u0437 \u0430\u0434\u043b\u044e\u0441\u0442\u0440\u0430\u0432\u0430\u043d\u043d\u0435\u043c \u0442\u043e\u043b\u044c\u043a\u0456 \u043d\u0435\u0432\u044f\u0434\u043e\u043c\u044b\u0445 \u0441\u043b\u043e\u045e<\/u><\/span><\/span><\/p>\n<ol>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0423\u0432\u0435\u0441\u0446\u0456 \u0442\u044d\u043a\u0441\u0442 \u0443 \u043f\u043e\u043b\u0435 \u045e\u0432\u043e\u0434\u0443 \u0442\u044d\u043a\u0441\u0442\u0443.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0423\u0432\u0435\u0441\u0446\u0456 \u0441\u043b\u043e\u0432\u044b \u0434\u043b\u044f \u043f\u0440\u0430\u0432\u0435\u0440\u043a\u0456, \u0440\u0430\u0437\u0434\u0437\u0435\u043b\u0435\u043d\u044b\u044f \u043f\u0440\u0430\u0431\u0435\u043b\u0430\u043c\u0456 \u0456\/\u0430\u0431\u043e \u0441\u0456\u043c\u0432\u0430\u043b\u0430\u043c\u0456 \u0442\u0430\u0431\u0443\u043b\u044f\u0446\u044b\u0456 \u0456 \u043f\u0435\u0440\u0430\u0432\u043e\u0434\u0443 \u0440\u0430\u0434\u043a\u0430, \u0443 \u043f\u043e\u043b\u0435 \u00ab\u0421\u043b\u043e\u045e\u043d\u0456\u043a\u00bb.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041f\u0435\u0440\u0430\u043a\u0430\u043d\u0430\u0446\u0446\u0430, \u0448\u0442\u043e \u043e\u043f\u0446\u044b\u044f \u00ab\u041d\u0435 \u0430\u0434\u043b\u044e\u0441\u0442\u0440\u043e\u045e\u0432\u0430\u0446\u044c \u0441\u043b\u043e\u0432\u044b, \u0437\u043d\u043e\u0439\u0434\u0437\u0435\u043d\u044b\u044f \u045e \u0441\u043b\u043e\u045e\u043d\u0456\u043a\u0443\u00bb \u045e\u043a\u043b\u044e\u0447\u0430\u043d\u0430\u044f. \u041a\u0430\u043b\u0456 \u043e\u043f\u0446\u044b\u044f \u0430\u0434\u043a\u043b\u044e\u0447\u0430\u043d\u0430, \u0430\u0434\u0437\u043d\u0430\u0447\u0446\u044c \u0430\u0434\u043f\u0430\u0432\u0435\u0434\u043d\u044b \u0441\u0446\u044f\u0436\u043e\u043a.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041d\u0430\u0446\u0456\u0441\u043d\u0443\u0446\u044c \u043a\u043d\u043e\u043f\u043a\u0443 \u00ab\u0410\u043f\u0440\u0430\u0446\u0430\u0432\u0430\u0446\u044c!\u00bb.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0410\u0442\u0440\u044b\u043c\u0430\u0446\u044c \u0432\u044b\u043d\u0456\u043a \u0443 \u043f\u043e\u043b\u0456 \u0432\u044b\u0434\u0430\u0447\u044b \u0432\u044b\u043d\u0456\u043a\u0430\u045e. \u041f\u0440\u044b \u043d\u0435\u0430\u0431\u0445\u043e\u0434\u043d\u0430\u0441\u0446\u0456 \u045e\u043d\u0435\u0441\u0446\u0456 \u0437\u043c\u0435\u043d\u044b \u045e \u0437\u044b\u0445\u043e\u0434\u043d\u044b \u0442\u044d\u043a\u0441\u0442 \u0456 \u043d\u0430\u0446\u0456\u0441\u043d\u0443\u0446\u044c \u043a\u043d\u043e\u043f\u043a\u0443 \u00ab\u0410\u043f\u0440\u0430\u0446\u0430\u0432\u0430\u0446\u044c!\u00bb \u044f\u0448\u0447\u044d \u0440\u0430\u0437.<\/span><\/span><\/li>\n<\/ol>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041c\u0430\u0433\u0447\u044b\u043c\u044b \u0432\u044b\u043d\u0456\u043a \u043f\u0440\u0430\u0446\u044b \u0441\u044d\u0440\u0432\u0456\u0441\u0430 \u0437\u0433\u043e\u0434\u043d\u0430 \u0437 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u043c \u0441\u0446\u044d\u043d\u0430\u0440\u044b\u0435\u043c \u043f\u0440\u0430\u0434\u0441\u0442\u0430\u045e\u043b\u0435\u043d\u044b \u043d\u0430 \u043c\u0430\u043b\u044e\u043d\u043a\u0443 3.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><a href=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-3.png\" class=\"grouped_elements\" rel=\"tc-fancybox-group8104\" title=\"Nearest Words Finder\"><img loading=\"lazy\" class=\"alignnone size-full wp-image-8107\" src=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-3.png\" alt=\"\" width=\"1191\" height=\"187\" srcset=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-3.png 1191w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-3-300x47.png 300w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-3-1024x161.png 1024w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/poshuk-najblizhejshyh-slo\u045e-3-768x121.png 768w\" sizes=\"(max-width: 1191px) 100vw, 1191px\" \/><\/a><\/span><\/p>\n<p align=\"center\"><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041c\u0430\u043b\u044e\u043d\u0430\u043a 3 \u2013 \u0412\u044b\u043d\u0456\u043a \u043f\u0440\u0430\u0446\u044b \u0441\u044d\u0440\u0432\u0456\u0441\u0430 \u00ab\u041f\u043e\u0448\u0443\u043a \u043d\u0430\u0439\u0431\u043b\u0456\u0436\u044d\u0439\u0448\u044b\u0445 \u0441\u043b\u043e\u045e\u00bb \u0437\u0433\u043e\u0434\u043d\u0430 \u0441\u0430 \u0441\u0446\u044d\u043d\u0430\u0440\u044b\u0435\u043c 2<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\"><b>\u0414\u043e\u0441\u0442\u0443\u043f \u0434\u0430 \u0441\u044d\u0440\u0432\u0456\u0441\u0430 \u043f\u0440\u0430\u0437 <\/b><\/span><span lang=\"en-US\"><b>API<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0414\u043b\u044f \u0434\u043e\u0441\u0442\u0443\u043f\u0443 \u0434\u0430 \u0441\u044d\u0440\u0432\u0456\u0441\u0430 \u00ab\u041f\u043e\u0448\u0443\u043a \u043d\u0430\u0439\u0431\u043b\u0456\u0436\u044d\u0439\u0448\u044b\u0445 \u0441\u043b\u043e\u045e\u00bb \u043f\u0440\u0430\u0437 <\/span><span lang=\"en-US\">API<\/span><span lang=\"be-BY\"> \u043d\u0435\u0430\u0431\u0445\u043e\u0434\u043d\u0430 \u0430\u0434\u043f\u0440\u0430\u0432\u0456\u0446\u044c <\/span><span lang=\"en-US\">AJAX<\/span><span lang=\"be-BY\">-\u0437\u0430\u043f\u044b\u0442 \u0442\u044b\u043f\u0443 <\/span><span lang=\"en-US\">POST<\/span><span lang=\"be-BY\"> \u043d\u0430 \u0430\u0434\u0440\u0430\u0441 <\/span><a href=\"https:\/\/corpus.by\/NearestWordsFinder\/api.php\"><span style=\"color: #0563c1;\"><u>https<\/u><\/span><span style=\"color: #0563c1;\"><u><span lang=\"be-BY\">:\/\/<\/span><\/u><\/span><span style=\"color: #0563c1;\"><u>corpus<\/u><\/span><span style=\"color: #0563c1;\"><u><span lang=\"be-BY\">.<\/span><\/u><\/span><span style=\"color: #0563c1;\"><u>by<\/u><\/span><span style=\"color: #0563c1;\"><u><span lang=\"be-BY\">\/<\/span><\/u><\/span><span style=\"color: #0563c1;\"><u>NearestWordsFinder<\/u><\/span><span style=\"color: #0563c1;\"><u><span lang=\"be-BY\">\/<\/span><\/u><\/span><span style=\"color: #0563c1;\"><u>api<\/u><\/span><span style=\"color: #0563c1;\"><u><span lang=\"be-BY\">.<\/span><\/u><\/span><span style=\"color: #0563c1;\"><u>php<\/u><\/span><\/a><span style=\"color: #0563c1;\"><u><span lang=\"be-BY\">.<\/span><\/u><\/span><span style=\"color: #0563c1;\"><span style=\"color: #000000;\"><span lang=\"be-BY\"> \u041f\u0440\u0430\u0437 \u043c\u0430\u0441\u0456\u045e <\/span><\/span><\/span><strong><em><span lang=\"en-US\">data<\/span><\/em><\/strong><span lang=\"be-BY\"> \u043f\u0435\u0440\u0430\u0434\u0430\u044e\u0446\u0446\u0430 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u044b\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b:<\/span><\/span><\/p>\n<ul>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><em><strong><span lang=\"en-US\">localization<\/span><\/strong><\/em><span lang=\"be-BY\"> \u2013 \u043c\u043e\u0432\u0430 \u043b\u0430\u043a\u0430\u043b\u0456\u0437\u0430\u0446\u044b\u0456. \u041c\u043e\u0436\u0430 \u043f\u0440\u044b\u043c\u0430\u0446\u044c \u0437\u043d\u0430\u0447\u044d\u043d\u043d\u0456 \u00ab<\/span><span lang=\"en-US\">en<\/span><span lang=\"be-BY\">\u00bb (\u0430\u043d\u0433\u0435\u043b\u044c\u0441\u043a\u0430\u044f \u043c\u043e\u0432\u0430) \u0456 \u00ab<\/span><span lang=\"en-US\">be<\/span><span lang=\"be-BY\">\u00bb (\u0431\u0435\u043b\u0430\u0440\u0443\u0441\u043a\u0430\u044f \u043c\u043e\u0432\u0430). <\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><em><strong><span lang=\"en-US\">text<\/span><\/strong><\/em><span lang=\"be-BY\"> \u2013 \u043f\u0430\u0441\u043b\u044f\u0434\u043e\u045e\u043d\u0430\u0441\u0446\u0456 \u0441\u0456\u043c\u0432\u0430\u043b\u0430\u045e, \u0440\u0430\u0437\u043c\u0435\u0448\u0447\u0430\u043d\u044b\u044f \u043f\u0430\u043c\u0456\u0436 \u0441\u0456\u043c\u0432\u0430\u043b\u0430\u043c\u0456 \u0432\u043e\u0434\u0441\u0442\u0443\u043f\u0443, \u044f\u043a\u0456\u044f \u0431\u0443\u0434\u0443\u0446\u044c \u043f\u0430\u0440\u0430\u045e\u043d\u043e\u045e\u0432\u0430\u0446\u0446\u0430 \u0441\u0430 \u0441\u043b\u043e\u045e\u043d\u0456\u043a\u0430\u0432\u044b\u043c\u0456 \u0437\u043d\u0430\u0447\u044d\u043d\u043d\u044f\u043c\u0456. <\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><em><strong><span lang=\"en-US\">dictionary<\/span><\/strong><\/em><span lang=\"be-BY\"> \u2013 \u043f\u0430\u0441\u043b\u044f\u0434\u043e\u045e\u043d\u0430\u0441\u0446\u0456 \u0441\u0456\u043c\u0432\u0430\u043b\u0430\u045e, \u0440\u0430\u0437\u043c\u0435\u0448\u0447\u0430\u043d\u044b\u044f \u043f\u0430\u043c\u0456\u0436 \u0441\u0456\u043c\u0432\u0430\u043b\u0430\u043c\u0456 \u0432\u043e\u0434\u0441\u0442\u0443\u043f\u0443, \u044f\u043a\u0456\u044f \u0431\u0443\u0434\u0443\u0446\u044c \u043f\u0430\u0440\u0430\u045e\u043d\u043e\u045e\u0432\u0430\u0446\u0446\u0430 \u0441\u0430 \u0441\u043b\u043e\u0432\u0430\u043c\u0456 \u0437\u044b\u0445\u043e\u0434\u043d\u0430\u0433\u0430 \u0442\u044d\u043a\u0441\u0442\u0443. <\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><em><strong><span lang=\"en-US\">knownWordsIgnore<\/span><\/strong><\/em><span lang=\"be-BY\"> \u2013 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440, \u044f\u043a\u0456 \u0430\u0434\u043a\u0430\u0437\u0432\u0430\u0435 \u0437\u0430 \u0430\u0434\u043b\u044e\u0441\u0442\u0440\u0430\u0432\u0430\u043d\u043d\u0435 \u0441\u043b\u043e\u045e \u0442\u044d\u043a\u0441\u0442\u0443, \u0437\u043d\u043e\u0439\u0434\u0437\u0435\u043d\u044b\u0445 \u0443 \u0441\u043b\u043e\u045e\u043d\u0456\u043a\u0443. \u041c\u043e\u0436\u0430 \u043f\u0440\u044b\u043c\u0430\u0446\u044c \u0437\u043d\u0430\u0447\u044d\u043d\u043d\u0456 \u00ab0\u00bb (\u0430\u0434\u043b\u044e\u0441\u0442\u0440\u043e\u045e\u0432\u0430\u0446\u044c) \u0456 \u00ab1\u00bb (\u043d\u0435 \u0430\u0434\u043b\u044e\u0441\u0442\u0440\u043e\u045e\u0432\u0430\u0446\u044c).<\/span><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u041f\u0440\u044b\u043a\u043b\u0430\u0434 <\/span><span lang=\"en-US\">AJAX-<\/span><span lang=\"be-BY\">\u0437\u0430\u043f\u044b\u0442\u0443:<\/span><\/span><\/p>\n<blockquote><p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">$.<span lang=\"en-US\">ajax<\/span>({<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">type<\/span>: \u201c<span lang=\"en-US\">POST<\/span>\u201d,<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">url: \u201c<\/span><span style=\"color: #333333;\"><span lang=\"en-US\">https:\/\/corpus.by\/NearestWordsFinder\/api.php<\/span><\/span><span lang=\"en-US\">\u201d,<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">data<\/span>:{<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">localization<\/span>\u201d: \u201c<span lang=\"en-US\">en<\/span>\u201d,<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">text<\/span>\u201d: \u201c\u0443\u043a\u043e\u0437 \u043e\u0431 \u043d\u0430\u043b\u043e\u0433\u0430\u0445 \u043d\u0430 \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439\u201d,<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">dictionary<\/span>\u201d: \u201c\u0443\u043a\u0430\u0437 \u043d\u0430\u043b\u043e\u0433\u0438 \u043d\u0430 \u043d\u0430\u043b\u043e\u0433\u0430\u0445 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0438 \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0442\u044c \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u043b \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439 \u043e\u0431\u201d,<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">knownWordsIgnore\u201d: 0<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">},<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">success: function(msg){ },<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">error: function() { }<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">});<\/span><\/span><\/p><\/blockquote>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0421\u0435\u0440\u0432\u0435\u0440 \u0432\u0435\u0440\u043d\u0435 <\/span><span lang=\"en-US\">JSON-<\/span><span lang=\"be-BY\"> \u0437 \u0437\u044b\u0445\u043e\u0434\u043d\u044b\u043c \u0442\u044d\u043a\u0441\u0442\u0430\u043c <\/span><span lang=\"en-US\">(<\/span>\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440<span lang=\"en-US\"><em><strong> text<\/strong><\/em>)<\/span><span lang=\"be-BY\"> \u0456 \u043a\u043e\u0434\u0430\u043c \u0432\u044b\u043d\u0456\u043a\u043e\u0432\u0430\u0439 <\/span><span lang=\"en-US\">HTML-<\/span><span lang=\"be-BY\">\u0442\u0430\u0431\u043b\u0456\u0446\u044b <\/span><span lang=\"en-US\">(<\/span>\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440<span lang=\"en-US\"><em><strong> result<\/strong><\/em>)<\/span><span lang=\"be-BY\"> \u0437 \u043f\u0430\u0440\u0430\u043c\u0456 \u00ab\u0441\u043b\u043e\u0432\u0430 \u2013 \u0430\u0434\u043f\u0430\u0432\u0435\u0434\u043d\u0456\u043a\u00bb, \u0434\u0437\u0435 \u0441\u043b\u043e\u0432\u0430 \u2013 \u0437\u044b\u0445\u043e\u0434\u043d\u0430\u044f \u043f\u0430\u0441\u043b\u044f\u0434\u043e\u045e\u043d\u0430\u0441\u0446\u044c \u0441\u0456\u043c\u0432\u0430\u043b\u0430\u045e, \u0430\u0434\u043f\u0430\u0432\u0435\u0434\u043d\u0456\u043a \u2013 \u0441\u043b\u043e\u045e\u043d\u0456\u043a\u0430\u0432\u0430\u044f \u043f\u0430\u0441\u043b\u044f\u0434\u043e\u045e\u043d\u0430\u0441\u0446\u044c \u0441\u0456\u043c\u0432\u0430\u043b\u0430\u045e, \u043d\u0430\u0439\u0431\u043b\u0456\u0436\u044d\u0439\u0448\u0430\u044f \u0434\u0430 \u0441\u043b\u043e\u0432\u0430 \u0437\u0433\u043e\u0434\u043d\u0430 \u0437 \u0430\u0434\u043b\u0435\u0433\u043b\u0430\u0441\u0446\u044e \u041b\u0435\u0432\u0435\u043d\u0448\u0442\u044d\u0439\u043d\u0430. \u041d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434, \u043f\u0430\u0432\u043e\u0434\u043b\u0435 \u043f\u0440\u044b\u0432\u0435\u0434\u0437\u0435\u043d\u0430\u0433\u0430 \u0432\u044b\u0448\u044d\u0439 <\/span><span lang=\"en-US\">AJAX<\/span><span lang=\"be-BY\">-\u0437\u0430\u043f\u044b\u0442\u0443 \u0431\u0443\u0434\u0437\u0435 \u0441\u0444\u0430\u0440\u043c\u0456\u0440\u0430\u0432\u0430\u043d\u044b \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u044b \u0430\u0434\u043a\u0430\u0437:<\/span><\/span><\/p>\n<blockquote><p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">[<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">{<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">text<\/span>\u201d: \u201c\u0443\u043a\u043e\u0437 \u043e\u0431 \u043d\u0430\u043b\u043e\u0433\u0430\u0445 \u043d\u0430 \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439\u201c,<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">result\u201d: &lt;table id=\\&#8221;resultTableId\\&#8221; class=\\&#8221;table table-sm table-striped\\&#8221; style=\\&#8221;table-layout: fixed; width: 100%;\\&#8221;&gt;&lt;thead&gt;&lt;tr&gt;&lt;th scope=\\&#8221;col\\&#8221;&gt;&lt;b&gt;Unknown word&lt;\\\/b&gt;&lt;\\\/th&gt;&lt;th scope=\\&#8221;col\\&#8221;&gt;&lt;b&gt;Possible variant&lt;\\\/b&gt;&lt;\\\/th&gt;&lt;\\\/tr&gt;&lt;\\\/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span> <span lang=\"en-US\">\u0443\u043a\u043e\u0437&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span> <span lang=\"en-US\">\u0443\u043a\u0430\u0437&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;\u043e\u0431 &lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043e\u0431<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043d\u0430\u043b\u043e\u0433\u0430\u0445<span lang=\"en-US\">&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043d\u0430\u043b\u043e\u0433\u0430\u0445<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043d\u0430<span lang=\"en-US\">&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043d\u0430<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438<span lang=\"en-US\">&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439<span lang=\"en-US\">&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;\\\/tbody&gt;&lt;\\\/table&gt; \u201c,<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">}<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">]<\/span><\/p><\/blockquote>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\"><b>\u0421\u043f\u0430\u0441\u044b\u043b\u043a\u0456 \u043d\u0430 \u043a\u0440\u044b\u043d\u0456\u0446\u044b<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0421\u0442\u0430\u0440\u043e\u043d\u043a\u0430 \u0441\u044d\u0440\u0432\u0456\u0441\u0430<\/span> \u2013 <span style=\"color: #0563c1;\"><u><a href=\"https:\/\/corpus.by\/NearestWordsFinder\/\">https:\/\/corpus.by\/NearestWordsFinder\/<\/a><\/u><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"be-BY\">\u0410\u0434\u043b\u0435\u0433\u043b\u0430\u0441\u0446\u044c \u041b\u0435\u0432\u0435\u043d\u0448\u0442\u044d\u0439\u043d\u0430 <\/span>(<span lang=\"be-BY\">\u0430\u0440\u0442\u044b\u043a\u0443\u043b \u0412\u0456\u043a\u0456\u043f\u0435\u0434\u044b\u0456<\/span>) \u2013 <span style=\"color: #0563c1;\"><u><a href=\"https:\/\/ru.wikipedia.org\/wiki\/%D0%A0%D0%B0%D1%81%D1%81%D1%82%D0%BE%D1%8F%D0%BD%D0%B8%D0%B5_%D0%9B%D0%B5%D0%B2%D0%B5%D0%BD%D1%88%D1%82%D0%B5%D0%B9%D0%BD%D0%B0\">https:\/\/ru.wikipedia.org\/wiki\/\u0420\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435_\u041b\u0435\u0432\u0435\u043d\u0448\u0442\u0435\u0439\u043d\u0430<\/a><\/u><\/span><\/span><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The <a href=\"https:\/\/corpus.by\/NearestWordsFinder\/?lang=en\"><strong>\u00abNearest Words Finder\u00bb<\/strong><\/a> service processes sequences of characters separated by indentation characters and compares them with user dictionary sequences. The result of the service work is an HTML table with \u00abword \u2013 match\u00bb pairs, where the word is the original sequence of characters, the match is the dictionary sequence of characters closest to the word according to Levenshtein distance.<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><b>Basic terms and concepts<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><em>Levenshtein distance<\/em> (editorial distance, distance of editing) is a metric that measures the difference between two sequences of characters. It is defined as the minimum number of one-character operations (namely, insertion, deletion, replacement) needed to transform one sequence of characters into another. In the general case, the operations used in this transformation can be assigned different prices (within the framework of the service work, the operations are equivalent). Widely used in information theory and computer linguistics. For the first time the task of measuring the distance of editing was posed in 1965 by the Soviet mathematician Vladimir Levenshtein.<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><b>Practical value<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The practical value of the service \u00abNearest Words Finder\u00bb is largely due to the sphere of using of the Levenshtein distance calculation. This calculation is actively used:<\/span><\/span><\/p>\n<ul>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">to correct errors in a word (in search engines, databases, during text entering, with automatic recognition of scanned text or speech);<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">for comparing text files (in case of such comparison, lines play the role of \u00abcharacters\u00bb, and files play the role of \u00ablines\u00bb);<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">in bioinformatics \u2013 to compare genes, chromosomes and proteins.<\/span><\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><b>Service features<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Some programming languages (such as PHP) have standard built-in functions for calculating the Levenshtein distance. Nevertheless, the implementation of these functions can seriously limit the length of the compared strings, as a result of that, processing of large strings leads to a deliberately incorrect result. In addition, there is no guarantee that this function is not implemented recursively. In many cases such an implementation may require undesired spending of system resources. Our calculation of the Levenshtein distance is implemented iteratively and has a high threshold for the length of the words being compared \u2013 1 million characters (the restriction is set only to exclude server overloads).<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">All the words that the service analyzes are pre-processed. Thus, the service considers \u00abwords\u00bb only those sequences that consist of basic Cyrillic and Latin characters, the main characters of the apostrophe, accent and hyphen, and also correspond to a certain pattern (which corresponds to the vast majority of words in natural languages). Processing is case-insensitive \u2013 for example, the characters \u00abk\u00bb and \u00abK\u00bb are considered the same character.<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><b>Service operation algorithm<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><i><u>Basic algorithm<\/u><\/i><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Algorithm input data:<\/span><\/span><\/p>\n<ul>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">User text input, Text;<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">User input of dictionary words, Dictionary;<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The value of the option responsible for including in the summary table words for which a full match is found in the dictionary, KnownWordsIgnore. If true, similar words will not appear in the output;<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Basic characters that the word can consist of (basic Cyrillic and Latin characters), MainChars;<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Additional characters that the word can consist of (characters of the apostrophe, main and secondary accent, hyphen), AdditionalChars.<\/span><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The beginning of the algorithm.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 1.1. Turning all words in the Dictionary to the lowercase. Dividing of the Dictionary into separate elements; the separator is all possible indentation characters.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 1.2. Creating a Result variable and recording the initial code of the final HTML table into it:<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">&lt;table id=&#8221;resultTableId&#8221; class=&#8221;table table-sm table-striped&#8221; style=&#8221;table-layout: fixed; width: 100%;&#8221;&gt;&lt;thead&gt;&lt;tr&gt;&lt;th scope=&#8221;col&#8221;&gt;&lt;b&gt;Unknown word&lt;\/b&gt;&lt;\/th&gt;&lt;th scope=&#8221;col&#8221;&gt;&lt;b&gt;Possible variant&lt;\/b&gt;&lt;\/th&gt;&lt;\/tr&gt;&lt;\/thead&gt;&lt;tbody&gt;&#8217;;<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 1.3. Creation of the ParagrapsArr array and filling it with lines of user input Text (user input, separated by a line feed character).<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2. For each Paragraph element in ParagraphsArr, perform steps 2.1. \u2013 2.3.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.1. Remove initial and last indent characters from Paragraph.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.2. Create the WordsArr array and fill it sequentially with all Paragraph elements matching the pattern [&lt;one MainChars character&gt; + &lt;0 or more MainChars or AdditionalChars characters&gt;] or the pattern [0 or more MainChars characters].<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.3. For all Word elements in WordsArr, perform steps 2.3.1. \u2013 2.3.3.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.3.1. Create a Nearest variable for the subsequent record of the word closest to Word and initialize it with a null value. Create the Distance variable for the subsequent recording of the Levenshtein distance and initialize it with the value 999999.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.3.2. For each Entry element in the Dictionary, perform steps 2.3.2.1. \u2013 2.3.2.2.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.3.2.1. Create a Lev variable and record in it the distance between Word and Entry determined by the Levenshtein distance calculation function.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.3.2.2. If Lev = 0, make the Nearest variable equal to Word, set the Distance value to 0, and end the cycle that started in step 2.3.2. Otherwise, if Lev &lt; Distance, make the Nearest variable equal to Entry, make the Distance variable equal to Lev. Repeating of this step at each iteration of the cycle that was started in step 2.3.2. will result in finding the word closest to Word.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.3.3. If KnownWordsIgnore = true, and Distance is not 0, add Result with a line of the form [\u00ab&lt;tr&gt; &lt;td style =&#8221;word-wrap: break-word&#8221;&gt;\u00bb+Word+\u00ab&lt;\/td&gt; &lt;td style =&#8221;word -wrap: break-word&#8221;&gt;\u00bb+Nearest+\u00ab&lt;\/td&gt; &lt;\/tr&gt;\u00bb]. If KnownWordsIgnore = false, add Result with this line anyway.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 3. Add Result with the string \u00ab&lt;\/tbody&gt; &lt;\/table&gt;\u00bb and finish the formation of the result table.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 4. Issue the result table to the user.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The end of the algorithm.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><i><u>Levenshtein distance calculation function<\/u><\/i><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Algorithm input data:<\/span><\/span><\/p>\n<ul>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The first word<\/span>, <span lang=\"en-US\">First<\/span>;<\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The second word<\/span>, <span lang=\"en-US\">Second<\/span>.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The beginning of the algorithm.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 1. If the words First and Second are identical, return 0 and finish the algorithm.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.1. Create a FLen variable and record the length of the First string to it. Create a variable SLen and record the length of the Second string to it.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 2.2. If FLen = 0 or SLen = 0, return FLen (in the case of SLen = 0) or SLen (in the case of FLen = 0) and finish the algorithm.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 3. Create a Dist array and fill it with numbers from 0 to SLen, each number is bigger than the previous by one.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 4. For I = 0; I &lt; FLen; I++ perform steps 4.1. \u2013 4.3.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 4.1 Create a ForDist array consisting of a single element equal to I+1.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 4.2 For J = 0; J &lt; SLen; J++ perform steps 4.2.1 \u2013 4.2.2.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 4.2.1. Create a numeric variable Cost to record the \u00abcost\u00bb adjustment of the replacement operation. If the character First[I] is equal to the character Second[J], initialize Cost with value 0, otherwise \u2013 with value 1.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 4.2.2. Attach a new element to ForDist (in the software implementation, the ordinal number of this element is J+1), equal to the minimum of the following three values:<\/span><\/span><\/p>\n<ul>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Dist[J+1] + 1 (cost of insertion operation);<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">ForDist[J] + 1 (cost of deletion operation);<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Dist[J] + Cost (cost of replacement operation).<\/span><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 4.3. Subsequently initialize the cells of the Dist array by the values of the corresponding cells of the ForDist array obtained after performing step 4.2. (the length of both arrays will be equal). This step is performed at the end of each iteration of the cycle that started in step 4.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Step 5. Return the value of the last cell in the Dist array (in the software implementation, the value of the cell whose ordinal number corresponds to SLen) and finish the algorithm.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The end of the algorithm.<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><b>User interface description<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The appearance of the user interface is shown in Figure 1.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><a href=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-1.png\" class=\"grouped_elements\" rel=\"tc-fancybox-group8104\" title=\"Nearest Words Finder\"><img loading=\"lazy\" class=\"alignnone size-full wp-image-8110\" src=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-1.png\" alt=\"\" width=\"1194\" height=\"419\" srcset=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-1.png 1194w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-1-300x105.png 300w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-1-1024x359.png 1024w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-1-768x270.png 768w\" sizes=\"(max-width: 1194px) 100vw, 1194px\" \/><\/a><\/span><\/p>\n<p align=\"center\"><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Figure 1 \u2013 Graphical interface of the service \u00abNearest Words Finder\u00bb<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The interface has the following areas:<\/span><\/span><\/p>\n<ul>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Input field for text for processing. This field is equipped with the buttons \u00abRefresh\u00bb (return default data) and \u00abClear\u00bb (delete all data);<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Field \u00abDictionary\u00bb for input of words for verification. This field is also equipped with the \u00abRefresh\u00bb and \u00abClear\u00bb buttons;<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The option \u00abDo not display words found in the dictionary\u00bb, which affects the final result of the service work;<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The button \u00abProcess!\u00bb, which starts processing and gives the opportunity to get results.<\/span><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">After clicking the \u00abProcess!\u00bb button and processing, a table with the results appears at the bottom of the screen.<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><b>User scenarios of work with the service<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><u>Scenario 1. Processing with displaying of all words of the source text<\/u><\/span><\/span><\/p>\n<ol>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Enter text in the text input field.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Enter the words for verification, separated by spaces and\/or tabs and line feeds, in the \u00abDictionary\u00bb field.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Make sure that the option \u00abDo not display words found in the dictionary\u00bb is disabled. If the option is enabled, clear the corresponding check box.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Click the \u00abProcess!\u00bb Button.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Obtain the result in the results issuing field. If necessary, make changes to the source text and click the \u00abProcess!\u00bb button again.<\/span><\/span><\/li>\n<\/ol>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The possible result of the service work according to this scenario is presented in Figure 2.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><a href=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-2.png\" class=\"grouped_elements\" rel=\"tc-fancybox-group8104\" title=\"Nearest Words Finder\"><img loading=\"lazy\" class=\"alignnone size-full wp-image-8111\" src=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-2.png\" alt=\"\" width=\"1188\" height=\"439\" srcset=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-2.png 1188w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-2-300x111.png 300w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-2-1024x378.png 1024w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-2-768x284.png 768w\" sizes=\"(max-width: 1188px) 100vw, 1188px\" \/><\/a><\/span><\/p>\n<p align=\"center\"><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Figure 2 \u2013 The result of the \u00abNearest Words Finder\u00bb service work according to the<\/span> <span lang=\"en-US\">scenario 1<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><u>Scenario 2. Processing with displaying only unknown words<\/u><\/span><\/span><\/p>\n<ol>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Enter text in the text input field.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Enter the words for verification, separated by spaces and\/or tabs and line feeds, in the \u00abDictionary\u00bb field.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Make sure that the option \u00abDo not display words found in the dictionary\u00bb is enabled. If the option is disabled, check the corresponding box.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Click the \u00abProcess!\u00bb button.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Obtain the result in the results issuing field. If necessary, make changes to the source text and click the \u00abProcess!\u00bb button again.<\/span><\/span><\/li>\n<\/ol>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The possible result of the service work according to this scenario is presented in Figure 3.<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><a href=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-3.png\" class=\"grouped_elements\" rel=\"tc-fancybox-group8104\" title=\"Nearest Words Finder\"><img loading=\"lazy\" class=\"alignnone size-full wp-image-8112\" src=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-3.png\" alt=\"\" width=\"1188\" height=\"220\" srcset=\"https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-3.png 1188w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-3-300x56.png 300w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-3-1024x190.png 1024w, https:\/\/ssrlab.by\/wp-content\/uploads\/2020\/07\/nearest-words-finder-3-768x142.png 768w\" sizes=\"(max-width: 1188px) 100vw, 1188px\" \/><\/a><\/span><\/p>\n<p align=\"center\"><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Figure 3 \u2013 The result of the \u00abNearest Words Finder\u00bb service work according to the scenario 2<\/span><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><b>Access to the service via the API<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">To access the \u00abNearest Words Finder\u00bb service via the API, you need to send an AJAX request of the POST type to the address https:\/\/corpus.by\/NearestWordsFinder\/api.php. The following parameters are passed through the <strong><em>data<\/em><\/strong> array:<\/span><\/span><\/p>\n<ul>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><em><strong>localization<\/strong><\/em> \u2013 localization language. It can take the values \u00aben\u00bb (English) and \u00abbe\u00bb (Belarusian).<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><em><strong>text<\/strong><\/em> \u2013 sequences of characters located between indent characters, which will be compared with dictionary values.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><em><strong>dictionary<\/strong><\/em> \u2013 sequences of characters located between indent characters, which will be compared with the words in the source text.<\/span><\/span><\/li>\n<li><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><em><strong>knownWordsIgnore<\/strong><\/em> \u2013 parameter responsible for displaying text words found in the dictionary. It can take the values \u00ab0\u00bb (display) and \u00ab1\u00bb (do not display).<\/span><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Example of AJAX request:<\/span><\/span><\/p>\n<blockquote><p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">$.ajax({<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">type: \u201cPOST\u201d,<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">url: \u201c<\/span><span style=\"color: #000000;\"><span lang=\"en-US\">https:\/\/corpus.by\/NearestWordsFinder\/api.php<\/span><\/span><span lang=\"en-US\">\u201d,<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">data<\/span>:{<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">localization<\/span>\u201d: \u201c<span lang=\"en-US\">en<\/span>\u201d,<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">text<\/span>\u201d: \u201c\u0443\u043a\u043e\u0437 \u043e\u0431 \u043d\u0430\u043b\u043e\u0433\u0430\u0445 \u043d\u0430 \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439\u201d,<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">dictionary<\/span>\u201d: \u201c\u0443\u043a\u0430\u0437 \u043d\u0430\u043b\u043e\u0433\u0438 \u043d\u0430 \u043d\u0430\u043b\u043e\u0433\u0430\u0445 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0438 \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0442\u044c \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u043b \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439 \u043e\u0431\u201d,<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">knownWordsIgnore\u201d: 0<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">},<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">success: function(msg){ },<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">error: function() { }<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">});<\/span><\/span><\/p><\/blockquote>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">The server will return a JSON array with the source text (parameter <em><strong>text<\/strong><\/em>) and the code of the resulting HTML table (<strong><em>result<\/em><\/strong> parameter) with the \u00abword \u2013 match\u00bb pairs, where the word is the original sequence of characters, the match is the dictionary sequence of characters closest to the word according to Levenshtein distance. For example, using the above AJAX request, the following response will be generated:<\/span><\/span><\/p>\n<blockquote><p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">[<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">{<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">text<\/span>\u201d: \u201c\u0443\u043a\u043e\u0437 \u043e\u0431 \u043d\u0430\u043b\u043e\u0433\u0430\u0445 \u043d\u0430 \u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439\u201c,<\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\">\u201c<span lang=\"en-US\">result\u201d: &lt;table id=\\&#8221;resultTableId\\&#8221; class=\\&#8221;table table-sm table-striped\\&#8221; style=\\&#8221;table-layout: fixed; width: 100%;\\&#8221;&gt;&lt;thead&gt;&lt;tr&gt;&lt;th scope=\\&#8221;col\\&#8221;&gt;&lt;b&gt;Unknown word&lt;\\\/b&gt;&lt;\\\/th&gt;&lt;th scope=\\&#8221;col\\&#8221;&gt;&lt;b&gt;Possible variant&lt;\\\/b&gt;&lt;\\\/th&gt;&lt;\\\/tr&gt;&lt;\\\/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span> <span lang=\"en-US\">\u0443\u043a\u043e\u0437&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span> <span lang=\"en-US\">\u0443\u043a\u0430\u0437&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;\u043e\u0431 &lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043e\u0431<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043d\u0430\u043b\u043e\u0433\u0430\u0445<span lang=\"en-US\">&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043d\u0430\u043b\u043e\u0433\u0430\u0445<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043d\u0430<span lang=\"en-US\">&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u043d\u0430<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438<span lang=\"en-US\">&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u0440\u0430\u0441\u0442\u0430\u043c\u043e\u0436\u0438\u0432\u0430\u043d\u0438\u0438<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;tr&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439<span lang=\"en-US\">&lt;\\\/td&gt;&lt;td style=\\&#8221;word-wrap: break-word\\&#8221;&gt;<\/span>\u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435\u0439<span lang=\"en-US\">&lt;\\\/td&gt;&lt;\\\/tr&gt;&lt;\\\/tbody&gt;&lt;\\\/table&gt; \u201c,<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">}<\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">]<\/span><\/span><\/p><\/blockquote>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\"><b>Source references<\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Service page \u2013 <\/span><span style=\"color: #0563c1;\"><u><a href=\"https:\/\/corpus.by\/NearestWordsFinder\/\"><span lang=\"en-US\">https:\/\/corpus.by\/NearestWordsFinder\/<\/span><\/a><\/u><\/span><\/span><\/p>\n<p><span style=\"font-family: helvetica, arial, sans-serif; font-size: 16px;\"><span lang=\"en-US\">Levenshtein distance (Wikipedia article) \u2013 <\/span><span style=\"color: #0563c1;\"><u><a href=\"https:\/\/en.wikipedia.org\/wiki\/Levenshtein_distance\"><span lang=\"en-US\">https:\/\/en.wikipedia.org\/wiki\/Levenshtein_distance<\/span><\/a><\/u><\/span><\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>Nearest Words Finder&#8221;> \u041c\u0430\u043b\u044e\u043d\u0430\u043a 1 \u2013 \u0413\u0440\u0430\u0444\u0456\u0447\u043d\u044b \u0456\u043d\u0442\u044d\u0440\u0444\u0435\u0439\u0441 \u0441\u044d\u0440\u0432\u0456\u0441\u0430 \u00ab\u041f\u043e\u0448\u0443\u043a \u043d\u0430\u0439\u0431\u043b\u0456\u0436\u044d\u0439\u0448\u044b\u0445 \u0441\u043b\u043e\u045e\u00bb \u0406\u043d\u0442\u044d\u0440\u0444\u0435\u0439\u0441 \u043c\u0430\u0435 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u044b\u044f \u0432\u043e\u0431\u043b\u0430\u0441\u0446\u0456: \u041f\u043e\u043b\u0435 \u045e\u0432\u043e\u0434\u0443 \u0442\u044d\u043a\u0441\u0442\u0443 \u0434\u043b\u044f \u0430\u043f\u0440\u0430\u0446\u043e\u045e\u043a\u0456. \u0414\u0430\u0434\u0437\u0435\u043d\u0430\u0435 \u043f\u043e\u043b\u0435 \u0437\u0430\u0431\u044f\u0441\u043f\u0435\u0447\u0430\u043d\u0430\u0435 \u043a\u043d\u043e\u043f\u043a\u0430\u043c\u0456 \u00ab\u0410\u0431\u043d\u0430\u0432\u0456\u0446\u044c\u00bb (\u0432\u044f\u0440\u043d\u0443\u0446\u044c \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u044f \u043f\u0430 \u0437\u043c\u0430\u045e\u0447\u0430\u043d\u043d\u0456) \u0456 \u00ab\u0410\u0447\u044b\u0441\u0446\u0456\u0446\u044c\u00bb (\u0432\u044b\u0434\u0430\u043b\u0456\u0446\u044c \u0443\u0441\u0435 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u044f); \u041f\u043e\u043b\u0435 \u00ab\u0421\u043b\u043e\u045e\u043d\u0456\u043a\u00bb \u0434\u043b\u044f \u045e\u0432\u043e\u0434\u0443 \u0441\u043b\u043e\u045e \u0434\u043b\u044f \u043f\u0440\u0430\u0432\u0435\u0440\u043a\u0456. \u0414\u0430\u0434\u0437\u0435\u043d\u0430\u0435 \u043f\u043e\u043b\u0435 \u0442\u0430\u043a\u0441\u0430\u043c\u0430 \u0437\u0430\u0431\u044f\u0441\u043f\u0435\u0447\u0430\u043d\u0430\u0435 \u043a\u043d\u043e\u043f\u043a\u0430\u043c\u0456 \u00ab\u0410\u0431\u043d\u0430\u0432\u0456\u0446\u044c\u00bb \u0456 \u00ab\u0410\u0447\u044b\u0441\u0446\u0456\u0446\u044c\u00bb; \u041e\u043f\u0446\u044b\u044f \u00ab\u041d\u0435 \u0430\u0434\u043b\u044e\u0441\u0442\u0440\u043e\u045e\u0432\u0430\u0446\u044c \u0441\u043b\u043e\u0432\u044b, \u0437\u043d\u043e\u0439\u0434\u0437\u0435\u043d\u044b\u044f [&hellip;]<\/p>\n<a class = \"excerpt\" href=\"https:\/\/ssrlab.by\/en\/8104\">Read more...<\/a>","protected":false},"author":14,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[5,290,292],"tags":[],"_links":{"self":[{"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/posts\/8104"}],"collection":[{"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/comments?post=8104"}],"version-history":[{"count":4,"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/posts\/8104\/revisions"}],"predecessor-version":[{"id":8120,"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/posts\/8104\/revisions\/8120"}],"wp:attachment":[{"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/media?parent=8104"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/categories?post=8104"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ssrlab.by\/en\/wp-json\/wp\/v2\/tags?post=8104"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}