{"id":12530,"date":"2025-12-09T10:37:58","date_gmt":"2025-12-09T01:37:58","guid":{"rendered":"https:\/\/keywordfinder.jp\/blog\/?p=12530"},"modified":"2025-12-09T10:37:58","modified_gmt":"2025-12-09T01:37:58","slug":"seo50_nlp_keyword_extraction","status":"publish","type":"post","link":"https:\/\/keywordfinder.jp\/blog\/seo50_nlp_keyword_extraction\/","title":{"rendered":"\u3010SEO\u4e0a\u7d1a\u8005\u5411\u3051\u3011\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\uff08NLP\uff09\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3067\u4e00\u6bb5\u4e0a\u306eSEO\u5bfe\u7b56\u3092\u3057\u3088\u3046\uff01"},"content":{"rendered":"<p>\u300c\u81a8\u5927\u306a\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u304b\u3089\u3001\u91cd\u8981\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u81ea\u52d5\u3067\u62bd\u51fa\u3057\u305f\u3044\u300d\u300cSEO\u5bfe\u7b56\u306e\u305f\u3081\u306b\u3001\u30b3\u30f3\u30c6\u30f3\u30c4\u306e\u4e3b\u984c\u3092\u7684\u78ba\u306b\u628a\u63e1\u3057\u305f\u3044\u300d\u2014\u2014\u305d\u3093\u306a\u8ab2\u984c\u3092\u62b1\u3048\u3066\u3044\u308b\u306a\u3089\u3001<strong>\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\uff08NLP\uff09\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa<\/strong>\u304c\u89e3\u6c7a\u306e\u9375\u3068\u306a\u308a\u307e\u3059\u3002<\/p>\n<p>\u5f93\u6765\u306e\u5358\u7d14\u306a\u5358\u8a9e\u30ab\u30a6\u30f3\u30c8\u3067\u306f\u3001\u6587\u8108\u3092\u7121\u8996\u3057\u305f\u4e0d\u6b63\u78ba\u306a\u7d50\u679c\u3057\u304b\u5f97\u3089\u308c\u307e\u305b\u3093\u3067\u3057\u305f\u3002\u3057\u304b\u3057\u3001NLP\u6280\u8853\u306e\u9032\u5316\u306b\u3088\u308a\u3001\u4eba\u9593\u306e\u8a00\u8a9e\u7406\u89e3\u306b\u8fd1\u3044\u7cbe\u5ea6\u3067\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u62bd\u51fa\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u672c\u8a18\u4e8b\u3067\u306f\u3001NLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u4ed5\u7d44\u307f\u304b\u3089\u5177\u4f53\u7684\u306a\u624b\u6cd5\u3001\u305d\u3057\u3066SEO\u3078\u306e\u5b9f\u8df5\u7684\u306a\u6d3b\u7528\u6cd5\u307e\u3067\u3001\u4f53\u7cfb\u7684\u306b\u89e3\u8aac\u3057\u307e\u3059\u3002<\/p>\n<!--h4--><img data-layzr=\"https:\/\/keywordfinder.jp\/blog\/wp-content\/uploads\/2025\/12\/7b8d327cd84c0d5fa63f454bc8b6562f.png\" alt=\"\u3010SEO\u4e0a\u7d1a\u8005\u5411\u3051\u3011\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\uff08NLP\uff09\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3067\u4e00\u6bb5\u4e0a\u306eSEO\u5bfe\u7b56\u3092\u3057\u3088\u3046\uff01\" class=\"attachment-icatch768 size-icatch768 wp-post-image\"><div id=\"rtoc-mokuji-wrapper\" class=\"rtoc-mokuji-content frame2 preset1 animation-fade rtoc_open default\" data-id=\"12530\" data-theme=\"THE THOR CHILD\">\n\t\t\t<div id=\"rtoc-mokuji-title\" class=\" rtoc_left\">\n\t\t\t<button class=\"rtoc_open_close rtoc_open\"><\/button>\n\t\t\t<span>\u76ee\u6b21<\/span>\n\t\t\t<\/div><ol class=\"rtoc-mokuji decimal_ol level-1\"><li class=\"rtoc-item\"><a href=\"#nlp-keyword-extraction\">NLP\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3068\u306f\uff1f<\/a><\/li><li class=\"rtoc-item\"><a href=\"#difference-traditional\">\u5f93\u6765\u306e\u62bd\u51fa\u3068\u306e\u9055\u3044<\/a><ul class=\"rtoc-mokuji mokuji_ul level-2\"><li class=\"rtoc-item\"><a href=\"#rtoc-3\">\u5f93\u6765\u306e\u624b\u6cd5\uff08\u30eb\u30fc\u30eb\u30d9\u30fc\u30b9\u30fb\u7d71\u8a08\u30d9\u30fc\u30b9\uff09<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-4\">NLP\u306b\u3088\u308b\u624b\u6cd5\u306e\u512a\u4f4d\u6027<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-5\">\u5177\u4f53\u7684\u306a\u6bd4\u8f03\u4f8b<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#mechanism\">NLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u4ed5\u7d44\u307f<\/a><ul class=\"rtoc-mokuji mokuji_ul level-2\"><li class=\"rtoc-item\"><a href=\"#rtoc-7\">\u30b9\u30c6\u30c3\u30d71\uff1a\u524d\u51e6\u7406\uff08Preprocessing\uff09<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-8\">\u30b9\u30c6\u30c3\u30d72\uff1a\u7279\u5fb4\u91cf\u62bd\u51fa\uff08Feature Extraction\uff09<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-9\">\u30b9\u30c6\u30c3\u30d73\uff1a\u30ad\u30fc\u30ef\u30fc\u30c9\u5019\u88dc\u306e\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-10\">\u30b9\u30c6\u30c3\u30d74\uff1a\u30ad\u30fc\u30ef\u30fc\u30c9\u306e\u9078\u5b9a\u3068\u51fa\u529b<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-11\">\u51e6\u7406\u30d5\u30ed\u30fc\u306e\u56f3\u89e3<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#ai-era-accuracy\">AI\u6642\u4ee3\u306eNLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u7cbe\u5ea6<\/a><ul class=\"rtoc-mokuji mokuji_ul level-2\"><li class=\"rtoc-item\"><a href=\"#rtoc-13\">\u5f93\u6765\u30e2\u30c7\u30eb\u3068Transformer\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u6bd4\u8f03<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-14\">BERT\u306b\u3088\u308b\u30d6\u30ec\u30fc\u30af\u30b9\u30eb\u30fc<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-15\">\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\uff08LLM\uff09\u306e\u6d3b\u7528<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-16\">\u7cbe\u5ea6\u5411\u4e0a\u306e\u305f\u3081\u306e\u30d9\u30b9\u30c8\u30d7\u30e9\u30af\u30c6\u30a3\u30b9<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#specific-methods\">\u5177\u4f53\u7684\u306aNLP\u62bd\u51fa\u624b\u6cd5<\/a><ul class=\"rtoc-mokuji mokuji_ul level-2\"><li class=\"rtoc-item\"><a href=\"#tf\">\u5358\u8a9e\u983b\u5ea6\u5206\u6790\uff08TF\uff09<\/a><ul class=\"rtoc-mokuji mokuji_none level-3\"><li class=\"rtoc-item\"><a href=\"#rtoc-19\">TF\u306e\u8a08\u7b97\u5f0f<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-20\">TF-IDF\u3078\u306e\u62e1\u5f35<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-21\">Python\u3067\u306e\u5b9f\u88c5\u4f8b<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#lda\">\u30c8\u30d4\u30c3\u30af\u30e2\u30c7\u30eb\uff08LDA\uff09<\/a><ul class=\"rtoc-mokuji mokuji_none level-3\"><li class=\"rtoc-item\"><a href=\"#rtoc-23\">LDA\u306e\u7279\u5fb4<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-24\">LDA\u306e\u4ed5\u7d44\u307f<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-25\">Python\u3067\u306e\u5b9f\u88c5\u4f8b<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#word2vec-bert\">\u5206\u6563\u8868\u73fe\uff08Word2Vec, BERT\uff09<\/a><ul class=\"rtoc-mokuji mokuji_none level-3\"><li class=\"rtoc-item\"><a href=\"#rtoc-27\">Word2Vec\u306e\u7279\u5fb4<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-28\">BERT\u306e\u7279\u5fb4<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-29\">BERT\u3092\u4f7f\u3063\u305f\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\uff08KeyBERT\uff09<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#cosine-similarity\">\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/a><ul class=\"rtoc-mokuji mokuji_none level-3\"><li class=\"rtoc-item\"><a href=\"#rtoc-31\">\u8a08\u7b97\u5f0f<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-32\">\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3078\u306e\u5fdc\u7528<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-33\">\u5b9f\u88c5\u4f8b<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#gpt-extraction\">GPT\u306b\u3088\u308bNLP\u62bd\u51fa<\/a><ul class=\"rtoc-mokuji mokuji_none level-3\"><li class=\"rtoc-item\"><a href=\"#rtoc-35\">GPT\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u5229\u70b9<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-36\">\u52b9\u679c\u7684\u306a\u30d7\u30ed\u30f3\u30d7\u30c8\u4f8b<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-37\">Python\uff08OpenAI API\uff09\u3067\u306e\u5b9f\u88c5\u4f8b<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#classification-methods\">\u62bd\u51fa\u8a9e\u306e\u5206\u985e\u30fb\u6574\u7406\u65b9\u6cd5<\/a><ul class=\"rtoc-mokuji mokuji_none level-3\"><li class=\"rtoc-item\"><a href=\"#rtoc-39\">1. \u610f\u5473\u7684\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-40\">2. \u968e\u5c64\u7684\u5206\u985e<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-41\">3. \u54c1\u8a5e\u30fb\u30a8\u30f3\u30c6\u30a3\u30c6\u30a3\u306b\u3088\u308b\u5206\u985e<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-42\">4. \u611f\u60c5\u30fb\u610f\u56f3\u306b\u3088\u308b\u5206\u985e<\/a><\/li><\/ul><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#seo-application\">NLP\u62bd\u51fa\u3092SEO\u306b\u3069\u3046\u6d3b\u304b\u3059\u304b\uff1f<\/a><ul class=\"rtoc-mokuji mokuji_ul level-2\"><li class=\"rtoc-item\"><a href=\"#rtoc-44\">1. \u30b3\u30f3\u30c6\u30f3\u30c4\u6700\u9069\u5316<\/a><ul class=\"rtoc-mokuji mokuji_none level-3\"><li class=\"rtoc-item\"><a href=\"#rtoc-45\">\u30ad\u30fc\u30ef\u30fc\u30c9\u5bc6\u5ea6\u306e\u6700\u9069\u5316<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-46\">LSI\u30ad\u30fc\u30ef\u30fc\u30c9\u306e\u6d3b\u7528<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-47\">2. \u7af6\u5408\u5206\u6790<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-48\">3. \u691c\u7d22\u610f\u56f3\u306e\u7406\u89e3<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-49\">4. \u30b3\u30f3\u30c6\u30f3\u30c4\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-50\">5. \u81ea\u52d5\u30bf\u30b0\u4ed8\u3051\u30fb\u30ab\u30c6\u30b4\u30ea\u5206\u985e<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-51\">6. \u30c8\u30ec\u30f3\u30c9\u5206\u6790\u3068\u30b3\u30f3\u30c6\u30f3\u30c4\u4f01\u753b<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-52\">\u5b9f\u8df5\u7684\u306aSEO\u30ef\u30fc\u30af\u30d5\u30ed\u30fc<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-53\">\u307e\u3068\u3081\uff1aNLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3067SEO\u3092\u5f37\u5316\u3057\u3088\u3046<\/a><ul class=\"rtoc-mokuji mokuji_ul level-2\"><li class=\"rtoc-item\"><a href=\"#rtoc-54\">\u95a2\u9023\u8a18\u4e8b<\/a><\/li><\/ul><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-55\">\u3088\u304f\u3042\u308b\u8cea\u554f\uff08FAQ\uff09<\/a><ul class=\"rtoc-mokuji mokuji_ul level-2\"><li class=\"rtoc-item\"><a href=\"#rtoc-56\">Q. NLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306f\u7121\u6599\u3067\u4f7f\u3048\u307e\u3059\u304b\uff1f<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-57\">Q. \u65e5\u672c\u8a9e\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3067\u6ce8\u610f\u3059\u3079\u304d\u70b9\u306f\uff1f<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-58\">Q. \u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u7d50\u679c\u306e\u7cbe\u5ea6\u3092\u4e0a\u3052\u308b\u306b\u306f\uff1f<\/a><\/li><li class=\"rtoc-item\"><a href=\"#rtoc-59\">Q. SEO\u3067\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3092\u4f7f\u3046\u5177\u4f53\u7684\u306a\u30e1\u30ea\u30c3\u30c8\u306f\uff1f<\/a><\/li><\/ul><\/li><\/ol><\/div><h2 id=\"nlp-keyword-extraction\">NLP\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3068\u306f\uff1f<\/h2>\n<p><strong>NLP\uff08Natural Language Processing\uff1a\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\uff09\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa<\/strong>\u3068\u306f\u3001\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u304c\u4eba\u9593\u306e\u8a00\u8a9e\u3092\u89e3\u6790\u3057\u3001\u30c6\u30ad\u30b9\u30c8\u4e2d\u304b\u3089\u91cd\u8981\u306a\u5358\u8a9e\u3084\u30d5\u30ec\u30fc\u30ba\u3092\u81ea\u52d5\u7684\u306b\u7279\u5b9a\u30fb\u62bd\u51fa\u3059\u308b\u6280\u8853\u3067\u3059\u3002<\/p>\n<p>\u3053\u306e\u6280\u8853\u306e\u672c\u8cea\u306f\u3001\u5358\u306a\u308b\u300c\u5358\u8a9e\u306e\u5207\u308a\u51fa\u3057\u300d\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u6587\u8108\u3084\u610f\u5473\u3092\u7406\u89e3\u3057\u305f\u4e0a\u3067\u3001\u305d\u306e\u30c6\u30ad\u30b9\u30c8\u3092\u4ee3\u8868\u3059\u308b<strong>\u6838\u5fc3\u7684\u306a\u30ad\u30fc\u30ef\u30fc\u30c9<\/strong>\u3092\u898b\u3064\u3051\u51fa\u3059\u3053\u3068\u306b\u3042\u308a\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001\u30cb\u30e5\u30fc\u30b9\u8a18\u4e8b\u304b\u3089\u4e3b\u984c\u3092\u628a\u63e1\u3057\u305f\u308a\u3001\u9867\u5ba2\u30ec\u30d3\u30e5\u30fc\u304b\u3089\u88fd\u54c1\u306e\u7279\u5fb4\u3092\u62bd\u51fa\u3057\u305f\u308a\u3059\u308b\u969b\u306b\u5a01\u529b\u3092\u767a\u63ee\u3057\u307e\u3059\u3002<\/p>\n<p>\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u7528\u9014\u306f\u591a\u5c90\u306b\u308f\u305f\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li><strong>\u60c5\u5831\u691c\u7d22<\/strong>\uff1a\u5927\u91cf\u306e\u6587\u66f8\u304b\u3089\u76ee\u7684\u306e\u60c5\u5831\u3092\u7d20\u65e9\u304f\u898b\u3064\u3051\u308b<\/li>\n<li><strong>\u6587\u66f8\u8981\u7d04<\/strong>\uff1a\u9577\u6587\u306e\u8981\u70b9\u3092\u81ea\u52d5\u7684\u306b\u62bd\u51fa\u3059\u308b<\/li>\n<li><strong>\u30c6\u30ad\u30b9\u30c8\u5206\u985e<\/strong>\uff1a\u6587\u66f8\u3092\u9069\u5207\u306a\u30ab\u30c6\u30b4\u30ea\u306b\u81ea\u52d5\u632f\u308a\u5206\u3051\u3059\u308b<\/li>\n<li><strong>SEO\u5bfe\u7b56<\/strong>\uff1a\u30b3\u30f3\u30c6\u30f3\u30c4\u306e\u4e3b\u984c\u3092\u691c\u7d22\u30a8\u30f3\u30b8\u30f3\u306b\u6b63\u78ba\u306b\u4f1d\u3048\u308b<\/li>\n<li><strong>\u30c8\u30ec\u30f3\u30c9\u5206\u6790<\/strong>\uff1aSNS\u3084\u53e3\u30b3\u30df\u304b\u3089\u8a71\u984c\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u628a\u63e1\u3059\u308b<\/li>\n<li><strong>\u9867\u5ba2\u5206\u6790<\/strong>\uff1aVOC\uff08Voice of Customer\uff09\u304b\u3089\u6f5c\u5728\u30cb\u30fc\u30ba\u3092\u767a\u898b\u3059\u308b<\/li>\n<\/ul>\n<p>\u8fd1\u5e74\u3067\u306f\u3001BERT\u3001GPT\u3068\u3044\u3063\u305f\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u306e\u767b\u5834\u306b\u3088\u308a\u3001\u62bd\u51fa\u7cbe\u5ea6\u306f\u98db\u8e8d\u7684\u306b\u5411\u4e0a\u3057\u3066\u3044\u307e\u3059\u3002\u5f93\u6765\u306f\u5c02\u9580\u77e5\u8b58\u304c\u5fc5\u8981\u3060\u3063\u305f\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u304c\u3001\u3088\u308a\u8eab\u8fd1\u306a\u6280\u8853\u306b\u306a\u308a\u3064\u3064\u3042\u308b\u306e\u3067\u3059\u3002<\/p>\n<h2 id=\"difference-traditional\">\u5f93\u6765\u306e\u62bd\u51fa\u3068\u306e\u9055\u3044<\/h2>\n<p>NLP\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u304c\u9769\u65b0\u7684\u3067\u3042\u308b\u7406\u7531\u3092\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u3001\u5f93\u6765\u306e\u624b\u6cd5\u3068\u306e\u9055\u3044\u3092\u898b\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n<h3 id=\"rtoc-3\" >\u5f93\u6765\u306e\u624b\u6cd5\uff08\u30eb\u30fc\u30eb\u30d9\u30fc\u30b9\u30fb\u7d71\u8a08\u30d9\u30fc\u30b9\uff09<\/h3>\n<p>\u5f93\u6765\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306f\u3001\u4e3b\u306b\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30a2\u30d7\u30ed\u30fc\u30c1\u3067\u884c\u308f\u308c\u3066\u3044\u307e\u3057\u305f\u3002<\/p>\n<table>\n<thead>\n<tr>\n<th>\u624b\u6cd5<\/th>\n<th>\u7279\u5fb4<\/th>\n<th>\u8ab2\u984c<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u5358\u7d14\u983b\u5ea6\u30ab\u30a6\u30f3\u30c8<\/td>\n<td>\u51fa\u73fe\u56de\u6570\u304c\u591a\u3044\u5358\u8a9e\u3092\u62bd\u51fa<\/td>\n<td>\u300c\u306e\u300d\u300c\u306f\u300d\u306a\u3069\u306e\u52a9\u8a5e\u304c\u4e0a\u4f4d\u306b<\/td>\n<\/tr>\n<tr>\n<td>\u30b9\u30c8\u30c3\u30d7\u30ef\u30fc\u30c9\u9664\u53bb<\/td>\n<td>\u4e0d\u8981\u8a9e\u30ea\u30b9\u30c8\u3067\u9664\u5916<\/td>\n<td>\u30ea\u30b9\u30c8\u4f5c\u6210\u30fb\u7ba1\u7406\u304c\u5927\u5909<\/td>\n<\/tr>\n<tr>\n<td>\u6b63\u898f\u8868\u73fe\u30de\u30c3\u30c1\u30f3\u30b0<\/td>\n<td>\u30d1\u30bf\u30fc\u30f3\u3067\u62bd\u51fa<\/td>\n<td>\u8907\u96d1\u306a\u30d1\u30bf\u30fc\u30f3\u306b\u5bfe\u5fdc\u56f0\u96e3<\/td>\n<\/tr>\n<tr>\n<td>\u54c1\u8a5e\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0<\/td>\n<td>\u540d\u8a5e\u306e\u307f\u3092\u62bd\u51fa<\/td>\n<td>\u91cd\u8981\u306a\u52d5\u8a5e\u30fb\u5f62\u5bb9\u8a5e\u3092\u898b\u843d\u3068\u3059<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u308c\u3089\u306e\u624b\u6cd5\u306b\u5171\u901a\u3059\u308b\u81f4\u547d\u7684\u306a\u554f\u984c\u306f\u3001<strong>\u6587\u8108\u3092\u7406\u89e3\u3067\u304d\u306a\u3044<\/strong>\u3053\u3068\u3067\u3059\u3002\u305f\u3068\u3048\u3070\u300cApple\u300d\u3068\u3044\u3046\u5358\u8a9e\u304c\u3001\u679c\u7269\u306e\u30ea\u30f3\u30b4\u306a\u306e\u304b\u3001IT\u4f01\u696d\u306e\u30a2\u30c3\u30d7\u30eb\u306a\u306e\u304b\u3092\u5224\u5225\u3067\u304d\u307e\u305b\u3093\u3002<\/p>\n<h3 id=\"rtoc-4\" >NLP\u306b\u3088\u308b\u624b\u6cd5\u306e\u512a\u4f4d\u6027<\/h3>\n<p>NLP\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306f\u3001\u4ee5\u4e0b\u306e\u70b9\u3067\u5f93\u6765\u624b\u6cd5\u3092\u5927\u304d\u304f\u4e0a\u56de\u308a\u307e\u3059\u3002<\/p>\n<ol>\n<li><strong>\u6587\u8108\u7406\u89e3<\/strong>\uff1a\u5468\u56f2\u306e\u5358\u8a9e\u3068\u306e\u95a2\u4fc2\u6027\u304b\u3089\u3001\u5358\u8a9e\u306e\u610f\u5473\u3092\u6b63\u78ba\u306b\u628a\u63e1\u3057\u307e\u3059\u3002\u300c\u9280\u884c\u300d\u304c\u91d1\u878d\u6a5f\u95a2\u306a\u306e\u304b\u3001\u5ddd\u306e\u571f\u624b\uff08\u30d0\u30f3\u30af\uff09\u306a\u306e\u304b\u3092\u6587\u8108\u304b\u3089\u5224\u65ad\u3067\u304d\u307e\u3059\u3002<\/li>\n<li><strong>\u540c\u7fa9\u8a9e\u30fb\u985e\u7fa9\u8a9e\u306e\u8a8d\u8b58<\/strong>\uff1a\u300c\u8eca\u300d\u300c\u81ea\u52d5\u8eca\u300d\u300c\u30ab\u30fc\u300d\u300c\u30af\u30eb\u30de\u300d\u3092\u540c\u3058\u6982\u5ff5\u3068\u3057\u3066\u6271\u3048\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u8868\u8a18\u63fa\u308c\u306b\u5de6\u53f3\u3055\u308c\u306a\u3044\u5206\u6790\u304c\u53ef\u80fd\u3067\u3059\u3002<\/li>\n<li><strong>\u8907\u5408\u8a9e\u30fb\u5c02\u9580\u7528\u8a9e\u306e\u62bd\u51fa<\/strong>\uff1a\u300c\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u300d\u300c\u6a5f\u68b0\u5b66\u7fd2\u300d\u3068\u3044\u3063\u305f\u8907\u5408\u8a9e\u3092\u4e00\u3064\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u3068\u3057\u3066\u8a8d\u8b58\u3057\u307e\u3059\u3002\u5358\u7d14\u306a\u5f62\u614b\u7d20\u89e3\u6790\u3067\u306f\u300c\u81ea\u7136\u300d\u300c\u8a00\u8a9e\u300d\u300c\u51e6\u7406\u300d\u3068\u5206\u5272\u3055\u308c\u3066\u3057\u307e\u3044\u307e\u3059\u3002<\/li>\n<li><strong>\u91cd\u8981\u5ea6\u306e\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0<\/strong>\uff1a\u5358\u306a\u308b\u51fa\u73fe\u56de\u6570\u3067\u306f\u306a\u304f\u3001\u6587\u66f8\u5168\u4f53\u306b\u304a\u3051\u308b\u91cd\u8981\u5ea6\u3092\u6570\u5024\u5316\u3057\u307e\u3059\u3002TF-IDF\u3084TextRank\u306a\u3069\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u6d3b\u7528\u3055\u308c\u307e\u3059\u3002<\/li>\n<li><strong>\u6559\u5e2b\u306a\u3057\u5b66\u7fd2\u3078\u306e\u5bfe\u5fdc<\/strong>\uff1a\u4e8b\u524d\u306b\u30e9\u30d9\u30eb\u4ed8\u3051\u3055\u308c\u305f\u30c7\u30fc\u30bf\u304c\u306a\u304f\u3066\u3082\u3001\u30c6\u30ad\u30b9\u30c8\u306e\u69cb\u9020\u304b\u3089\u91cd\u8981\u8a9e\u3092\u767a\u898b\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ol>\n<h3 id=\"rtoc-5\" >\u5177\u4f53\u7684\u306a\u6bd4\u8f03\u4f8b<\/h3>\n<p>\u4ee5\u4e0b\u306e\u30c6\u30ad\u30b9\u30c8\u3067\u3001\u5f93\u6765\u624b\u6cd5\u3068NLP\u624b\u6cd5\u306e\u62bd\u51fa\u7d50\u679c\u3092\u6bd4\u8f03\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<blockquote>\n<p>\u300cApple\u306e\u65b0\u578biPhone\u306f\u3001AI\u3092\u6d3b\u7528\u3057\u305f\u30ab\u30e1\u30e9\u6a5f\u80fd\u304c\u7279\u5fb4\u3060\u3002Google\u3082\u540c\u69d8\u306e\u6280\u8853\u3092\u958b\u767a\u3057\u3066\u3044\u308b\u3002\u30b9\u30de\u30fc\u30c8\u30d5\u30a9\u30f3\u5e02\u5834\u306e\u7af6\u4e89\u306f\u6fc0\u5316\u3057\u3066\u3044\u308b\u3002\u300d<\/p>\n<\/blockquote>\n<p><strong>\u5f93\u6765\u624b\u6cd5\uff08\u983b\u5ea6\u30d9\u30fc\u30b9\uff09\u306e\u7d50\u679c\uff1a<\/strong><\/p>\n<p>\u300c\u306e\u300d\u300c\u306f\u300d\u300c\u3092\u300d\u300c\u304c\u300d\u306a\u3069\u52a9\u8a5e\u304c\u4e0a\u4f4d\u3001\u6709\u610f\u7fa9\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u62bd\u51fa\u56f0\u96e3<\/p>\n<p><strong>NLP\u624b\u6cd5\u306e\u7d50\u679c\uff1a<\/strong><\/p>\n<p>\u300cApple\u300d\u300ciPhone\u300d\u300cAI\u300d\u300c\u30ab\u30e1\u30e9\u6a5f\u80fd\u300d\u300cGoogle\u300d\u300c\u30b9\u30de\u30fc\u30c8\u30d5\u30a9\u30f3\u5e02\u5834\u300d\u306a\u3069\u3001\u5185\u5bb9\u3092\u7684\u78ba\u306b\u8868\u3059\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u62bd\u51fa<\/p>\n<p>\u3053\u306e\u3088\u3046\u306b\u3001NLP\u306f\u6587\u66f8\u306e\u672c\u8cea\u3092\u6349\u3048\u305f\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3092\u53ef\u80fd\u306b\u3057\u307e\u3059\u3002<\/p>\n<h2 id=\"mechanism\">NLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u4ed5\u7d44\u307f<\/h2>\n<p>NLP\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306f\u3001\u8907\u6570\u306e\u51e6\u7406\u30b9\u30c6\u30c3\u30d7\u3092\u7d4c\u3066\u5b9f\u884c\u3055\u308c\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u305d\u306e\u4ed5\u7d44\u307f\u3092\u6bb5\u968e\u7684\u306b\u89e3\u8aac\u3057\u307e\u3059\u3002<\/p>\n<h3 id=\"rtoc-7\" >\u30b9\u30c6\u30c3\u30d71\uff1a\u524d\u51e6\u7406\uff08Preprocessing\uff09<\/h3>\n<p>\u751f\u306e\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u306f\u3001\u305d\u306e\u307e\u307e\u3067\u306f\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u304c\u51e6\u7406\u3057\u306b\u304f\u3044\u5f62\u5f0f\u3067\u3059\u3002\u307e\u305a\u4ee5\u4e0b\u306e\u524d\u51e6\u7406\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n<ul>\n<li><strong>\u30af\u30ea\u30fc\u30cb\u30f3\u30b0<\/strong>\uff1aHTML\u30bf\u30b0\u3001\u7279\u6b8a\u6587\u5b57\u3001URL\u306a\u3069\u306e\u30ce\u30a4\u30ba\u3092\u9664\u53bb<\/li>\n<li><strong>\u6b63\u898f\u5316<\/strong>\uff1a\u5168\u89d2\u30fb\u534a\u89d2\u306e\u7d71\u4e00\u3001\u5927\u6587\u5b57\u30fb\u5c0f\u6587\u5b57\u306e\u7d71\u4e00<\/li>\n<li><strong>\u5f62\u614b\u7d20\u89e3\u6790<\/strong>\uff1a\u65e5\u672c\u8a9e\u306e\u5834\u5408\u3001MeCab\u3084Janome\u3067\u5358\u8a9e\u306b\u5206\u5272<\/li>\n<li><strong>\u30b9\u30c8\u30c3\u30d7\u30ef\u30fc\u30c9\u9664\u53bb<\/strong>\uff1a\u300c\u3053\u308c\u300d\u300c\u305d\u308c\u300d\u306a\u3069\u306e\u6a5f\u80fd\u8a9e\u3092\u9664\u5916<\/li>\n<li><strong>\u30b9\u30c6\u30df\u30f3\u30b0\/\u30ec\u30f3\u30de\u30bf\u30a4\u30ba<\/strong>\uff1a\u300c\u8d70\u3063\u305f\u300d\u300c\u8d70\u308b\u300d\u3092\u539f\u5f62\u306b\u7d71\u4e00<\/li>\n<\/ul>\n<h3 id=\"rtoc-8\" >\u30b9\u30c6\u30c3\u30d72\uff1a\u7279\u5fb4\u91cf\u62bd\u51fa\uff08Feature Extraction\uff09<\/h3>\n<p>\u524d\u51e6\u7406\u3055\u308c\u305f\u30c6\u30ad\u30b9\u30c8\u304b\u3089\u3001\u6570\u5024\u7684\u306a\u7279\u5fb4\u91cf\u3092\u62bd\u51fa\u3057\u307e\u3059\u3002\u4ee3\u8868\u7684\u306a\u624b\u6cd5\u306b\u306f\u4ee5\u4e0b\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li><strong>Bag of Words\uff08BoW\uff09<\/strong>\uff1a\u5358\u8a9e\u306e\u51fa\u73fe\u6709\u7121\u30fb\u56de\u6570\u3092\u30d9\u30af\u30c8\u30eb\u5316<\/li>\n<li><strong>TF-IDF<\/strong>\uff1a\u5358\u8a9e\u306e\u983b\u5ea6\u3068\u5e0c\u5c11\u6027\u3092\u639b\u3051\u5408\u308f\u305b\u305f\u30b9\u30b3\u30a2<\/li>\n<li><strong>Word Embeddings<\/strong>\uff1a\u5358\u8a9e\u3092\u610f\u5473\u3092\u4fdd\u6301\u3057\u305f\u5bc6\u306a\u30d9\u30af\u30c8\u30eb\u306b\u5909\u63db<\/li>\n<li><strong>\u6587\u8108\u57cb\u3081\u8fbc\u307f<\/strong>\uff1aBERT\u306a\u3069\u3067\u6587\u8108\u306b\u5fdc\u3058\u305f\u30d9\u30af\u30c8\u30eb\u3092\u751f\u6210<\/li>\n<\/ul>\n<h3 id=\"rtoc-9\" >\u30b9\u30c6\u30c3\u30d73\uff1a\u30ad\u30fc\u30ef\u30fc\u30c9\u5019\u88dc\u306e\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0<\/h3>\n<p>\u62bd\u51fa\u3055\u308c\u305f\u7279\u5fb4\u91cf\u3092\u3082\u3068\u306b\u3001\u5404\u5358\u8a9e\u30fb\u30d5\u30ec\u30fc\u30ba\u306e\u91cd\u8981\u5ea6\u3092\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0\u3057\u307e\u3059\u3002<\/p>\n<ul>\n<li><strong>\u7d71\u8a08\u7684\u624b\u6cd5<\/strong>\uff1aTF-IDF\u3001YAKE\u3001RAKE\u306a\u3069<\/li>\n<li><strong>\u30b0\u30e9\u30d5\u30d9\u30fc\u30b9\u624b\u6cd5<\/strong>\uff1aTextRank\u3001PositionRank\u3001MultipartiteRank\u306a\u3069<\/li>\n<li><strong>\u6a5f\u68b0\u5b66\u7fd2\u624b\u6cd5<\/strong>\uff1a\u5206\u985e\u30e2\u30c7\u30eb\u3067\u30ad\u30fc\u30ef\u30fc\u30c9\u304b\u5426\u304b\u3092\u5224\u5b9a<\/li>\n<li><strong>\u6df1\u5c64\u5b66\u7fd2\u624b\u6cd5<\/strong>\uff1aBERT\u3084GPT\u3092\u4f7f\u3063\u305f\u7cfb\u5217\u30e9\u30d9\u30ea\u30f3\u30b0<\/li>\n<\/ul>\n<h3 id=\"rtoc-10\" >\u30b9\u30c6\u30c3\u30d74\uff1a\u30ad\u30fc\u30ef\u30fc\u30c9\u306e\u9078\u5b9a\u3068\u51fa\u529b<\/h3>\n<p>\u30b9\u30b3\u30a2\u306e\u9ad8\u3044\u9806\u306b\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u9078\u5b9a\u3057\u3001\u51fa\u529b\u3057\u307e\u3059\u3002\u3053\u306e\u969b\u3001\u4ee5\u4e0b\u306e\u5f8c\u51e6\u7406\u304c\u884c\u308f\u308c\u308b\u3053\u3068\u3082\u3042\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li><strong>\u91cd\u8907\u6392\u9664<\/strong>\uff1a\u300cAI\u300d\u3068\u300c\u4eba\u5de5\u77e5\u80fd\u300d\u306e\u91cd\u8907\u3092\u7d71\u5408<\/li>\n<li><strong>\u95be\u5024\u306b\u3088\u308b\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0<\/strong>\uff1a\u4e00\u5b9a\u30b9\u30b3\u30a2\u4ee5\u4e0a\u306e\u307f\u62bd\u51fa<\/li>\n<li><strong>\u4e0a\u4f4dN\u4ef6\u306e\u51fa\u529b<\/strong>\uff1aTop-5\u3001Top-10\u306a\u3069\u3067\u5236\u9650<\/li>\n<li><strong>\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/strong>\uff1a\u985e\u4f3c\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u30b0\u30eb\u30fc\u30d7\u5316<\/li>\n<\/ul>\n<h3 id=\"rtoc-11\" >\u51e6\u7406\u30d5\u30ed\u30fc\u306e\u56f3\u89e3<\/h3>\n<pre>\r\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\r\n\u2502   \u539f\u6587      \u2502 \u2192 \u2502  \u524d\u51e6\u7406     \u2502 \u2192 \u2502 \u7279\u5fb4\u91cf\u62bd\u51fa  \u2502 \u2192 \u2502 \u30b9\u30b3\u30a2\u30ea\u30f3\u30b0 \u2502\r\n\u2502 \u30c6\u30ad\u30b9\u30c8    \u2502    \u2502 \u30af\u30ea\u30fc\u30cb\u30f3\u30b0 \u2502    \u2502 TF-IDF\u7b49   \u2502    \u2502 TextRank\u7b49  \u2502\r\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518    \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518    \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518    \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\r\n                                                              \u2193\r\n                   \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\r\n                   \u2502 \u30ad\u30fc\u30ef\u30fc\u30c9  \u2502 \u2190 \u2502  \u5f8c\u51e6\u7406     \u2502\r\n                   \u2502 \u30ea\u30b9\u30c8\u51fa\u529b  \u2502    \u2502 \u91cd\u8907\u6392\u9664\u7b49  \u2502\r\n                   \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518    \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\r\n<\/pre>\n<h2 id=\"ai-era-accuracy\">AI\u6642\u4ee3\u306eNLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u7cbe\u5ea6<\/h2>\n<p>2018\u5e74\u306eBERT\u767b\u5834\u4ee5\u964d\u3001NLP\u306e\u7cbe\u5ea6\u306f\u5287\u7684\u306b\u5411\u4e0a\u3057\u307e\u3057\u305f\u3002\u3053\u3053\u3067\u306f\u3001\u6700\u65b0\u306eAI\u6280\u8853\u304c\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306b\u3082\u305f\u3089\u3057\u305f\u5909\u9769\u3092\u89e3\u8aac\u3057\u307e\u3059\u3002<\/p>\n<h3 id=\"rtoc-13\" >\u5f93\u6765\u30e2\u30c7\u30eb\u3068Transformer\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u6bd4\u8f03<\/h3>\n<p>\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u8a55\u4fa1\u6307\u6a19\u3068\u3057\u3066\u3001Precision\uff08\u9069\u5408\u7387\uff09\u3001Recall\uff08\u518d\u73fe\u7387\uff09\u3001F1\u30b9\u30b3\u30a2\u304c\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/p>\n<table>\n<thead>\n<tr>\n<th>\u624b\u6cd5<\/th>\n<th>F1\u30b9\u30b3\u30a2\uff08\u76ee\u5b89\uff09<\/th>\n<th>\u7279\u5fb4<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>TF-IDF<\/td>\n<td>0.10\u301c0.15<\/td>\n<td>\u30b7\u30f3\u30d7\u30eb\u3060\u304c\u6587\u8108\u975e\u8003\u616e<\/td>\n<\/tr>\n<tr>\n<td>TextRank<\/td>\n<td>0.12\u301c0.18<\/td>\n<td>\u30b0\u30e9\u30d5\u30d9\u30fc\u30b9\u3067\u95a2\u4fc2\u6027\u3092\u8003\u616e<\/td>\n<\/tr>\n<tr>\n<td>YAKE<\/td>\n<td>0.15\u301c0.20<\/td>\n<td>\u6559\u5e2b\u306a\u3057\u3067\u9ad8\u7cbe\u5ea6<\/td>\n<\/tr>\n<tr>\n<td>BERT-based<\/td>\n<td>0.25\u301c0.35<\/td>\n<td>\u6587\u8108\u7406\u89e3\u3067\u5927\u5e45\u5411\u4e0a<\/td>\n<\/tr>\n<tr>\n<td>GPT-4\/Claude<\/td>\n<td>0.30\u301c0.40+<\/td>\n<td>\u6700\u9ad8\u7cbe\u5ea6\u3001\u67d4\u8edf\u306a\u51fa\u529b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u203b\u30b9\u30b3\u30a2\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3084\u8a55\u4fa1\u6761\u4ef6\u306b\u3088\u308a\u5909\u52d5\u3057\u307e\u3059<\/p>\n<h3 id=\"rtoc-14\" >BERT\u306b\u3088\u308b\u30d6\u30ec\u30fc\u30af\u30b9\u30eb\u30fc<\/h3>\n<p>BERT\uff08Bidirectional Encoder Representations from Transformers\uff09\u306f\u3001\u4ee5\u4e0b\u306e\u70b9\u3067\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306b\u9769\u547d\u3092\u3082\u305f\u3089\u3057\u307e\u3057\u305f\u3002<\/p>\n<ol>\n<li><strong>\u53cc\u65b9\u5411\u6587\u8108\u7406\u89e3<\/strong>\uff1a\u5358\u8a9e\u306e\u524d\u5f8c\u4e21\u65b9\u306e\u6587\u8108\u3092\u540c\u6642\u306b\u8003\u616e\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u300cbank\u300d\u304c\u300c\u9280\u884c\u300d\u304b\u300c\u571f\u624b\u300d\u304b\u3092\u6b63\u78ba\u306b\u5224\u5b9a\u3067\u304d\u307e\u3059\u3002<\/li>\n<li><strong>\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb<\/strong>\uff1a\u5927\u898f\u6a21\u30b3\u30fc\u30d1\u30b9\u3067\u5b66\u7fd2\u6e08\u307f\u306e\u305f\u3081\u3001\u5c11\u91cf\u306e\u30c7\u30fc\u30bf\u3067\u3082\u9ad8\u7cbe\u5ea6\u306a\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u304c\u53ef\u80fd\u3067\u3059\u3002<\/li>\n<li><strong>\u56fa\u6709\u8868\u73fe\u62bd\u51fa\u3078\u306e\u5fdc\u7528<\/strong>\uff1a\u4eba\u540d\u3001\u5730\u540d\u3001\u7d44\u7e54\u540d\u306a\u3069\u306e\u56fa\u6709\u8868\u73fe\u3092\u9ad8\u7cbe\u5ea6\u3067\u62bd\u51fa\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ol>\n<p>\u65e5\u672c\u8a9e\u3067\u306f\u3001\u6771\u5317\u5927\u5b66\u304c\u516c\u958b\u3057\u3066\u3044\u308b\u300ccl-tohoku\/bert-base-japanese\u300d\u304c\u5e83\u304f\u5229\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<h3 id=\"rtoc-15\" >\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\uff08LLM\uff09\u306e\u6d3b\u7528<\/h3>\n<p>GPT-4\u3084Claude\u3068\u3044\u3063\u305fLLM\u306f\u3001\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306b\u304a\u3044\u3066\u3082\u9a5a\u7570\u7684\u306a\u6027\u80fd\u3092\u767a\u63ee\u3057\u307e\u3059\u3002<\/p>\n<ul>\n<li><strong>\u30bc\u30ed\u30b7\u30e7\u30c3\u30c8\/Few-shot\u5b66\u7fd2<\/strong>\uff1a\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u4e0e\u3048\u308b\u3060\u3051\u3067\u3001\u4e8b\u524d\u5b66\u7fd2\u306a\u3057\u306b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u304c\u53ef\u80fd<\/li>\n<li><strong>\u69cb\u9020\u5316\u51fa\u529b<\/strong>\uff1aJSON\u5f62\u5f0f\u3067\u306e\u51fa\u529b\u6307\u5b9a\u306b\u3088\u308a\u3001\u5f8c\u7d9a\u51e6\u7406\u3068\u306e\u9023\u643a\u304c\u5bb9\u6613<\/li>\n<li><strong>\u30de\u30eb\u30c1\u30bf\u30b9\u30af\u51e6\u7406<\/strong>\uff1a\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3068\u540c\u6642\u306b\u3001\u8981\u7d04\u3084\u5206\u985e\u3082\u5b9f\u884c\u53ef\u80fd<\/li>\n<li><strong>\u8aac\u660e\u53ef\u80fd\u6027<\/strong>\uff1a\u306a\u305c\u305d\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u9078\u3093\u3060\u304b\u3001\u7406\u7531\u306e\u8aac\u660e\u3082\u53ef\u80fd<\/li>\n<\/ul>\n<h3 id=\"rtoc-16\" >\u7cbe\u5ea6\u5411\u4e0a\u306e\u305f\u3081\u306e\u30d9\u30b9\u30c8\u30d7\u30e9\u30af\u30c6\u30a3\u30b9<\/h3>\n<p>\u6700\u65b0\u306eAI\u6280\u8853\u3092\u6d3b\u7528\u3057\u3066\u3082\u3001\u4ee5\u4e0b\u306e\u30dd\u30a4\u30f3\u30c8\u3092\u62bc\u3055\u3048\u308b\u3053\u3068\u3067\u3001\u3055\u3089\u306b\u7cbe\u5ea6\u3092\u5411\u4e0a\u3055\u305b\u3089\u308c\u307e\u3059\u3002<\/p>\n<ol>\n<li><strong>\u30c9\u30e1\u30a4\u30f3\u7279\u5316<\/strong>\uff1a\u696d\u754c\u30fb\u5206\u91ce\u306b\u7279\u5316\u3057\u305f\u8f9e\u66f8\u3084\u4e8b\u524d\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u4f7f\u7528<\/li>\n<li><strong>\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb<\/strong>\uff1a\u8907\u6570\u624b\u6cd5\u306e\u7d50\u679c\u3092\u7d71\u5408\u3057\u3066\u4fe1\u983c\u6027\u3092\u5411\u4e0a<\/li>\n<li><strong>\u30d2\u30e5\u30fc\u30de\u30f3\u30fb\u30a4\u30f3\u30fb\u30b6\u30fb\u30eb\u30fc\u30d7<\/strong>\uff1a\u4eba\u9593\u306b\u3088\u308b\u30ec\u30d3\u30e5\u30fc\u3068\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3092\u7d44\u307f\u8fbc\u3080<\/li>\n<li><strong>\u7d99\u7d9a\u7684\u6539\u5584<\/strong>\uff1a\u62bd\u51fa\u7d50\u679c\u3092\u5b9a\u671f\u7684\u306b\u8a55\u4fa1\u3057\u3001\u30e2\u30c7\u30eb\u3092\u66f4\u65b0<\/li>\n<\/ol>\n<h2 id=\"specific-methods\">\u5177\u4f53\u7684\u306aNLP\u62bd\u51fa\u624b\u6cd5<\/h2>\n<p>\u3053\u3053\u304b\u3089\u306f\u3001\u5b9f\u969b\u306b\u4f7f\u308f\u308c\u3066\u3044\u308b\u4e3b\u8981\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u624b\u6cd5\u3092\u3001\u5177\u4f53\u7684\u306a\u30b3\u30fc\u30c9\u4f8b\u3068\u3068\u3082\u306b\u89e3\u8aac\u3057\u307e\u3059\u3002<\/p>\n<h3 id=\"tf\">\u5358\u8a9e\u983b\u5ea6\u5206\u6790\uff08TF\uff09<\/h3>\n<p><strong>TF\uff08Term Frequency\uff1a\u5358\u8a9e\u983b\u5ea6\uff09<\/strong>\u306f\u3001\u6700\u3082\u30b7\u30f3\u30d7\u30eb\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u57fa\u790e\u3068\u306a\u308b\u624b\u6cd5\u3067\u3059\u3002\u6587\u66f8\u5185\u3067\u7279\u5b9a\u306e\u5358\u8a9e\u304c\u4f55\u56de\u51fa\u73fe\u3059\u308b\u304b\u3092\u30ab\u30a6\u30f3\u30c8\u3057\u307e\u3059\u3002<\/p>\n<h4 id=\"rtoc-19\" >TF\u306e\u8a08\u7b97\u5f0f<\/h4>\n<pre>\r\nTF(t, d) = \u6587\u66f8d\u306b\u304a\u3051\u308b\u5358\u8a9et\u306e\u51fa\u73fe\u56de\u6570 \/ \u6587\u66f8d\u306e\u7dcf\u5358\u8a9e\u6570\r\n<\/pre>\n<h4 id=\"rtoc-20\" >TF-IDF\u3078\u306e\u62e1\u5f35<\/h4>\n<p>TF\u5358\u4f53\u3067\u306f\u300c\u306e\u300d\u300c\u306f\u300d\u306a\u3069\u306e\u983b\u51fa\u8a9e\u304c\u4e0a\u4f4d\u306b\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3059\u3002\u3053\u308c\u3092\u89e3\u6c7a\u3059\u308b\u306e\u304c<strong>TF-IDF<\/strong>\u3067\u3059\u3002<\/p>\n<pre>\r\nIDF(t) = log(\u5168\u6587\u66f8\u6570 \/ \u5358\u8a9et\u3092\u542b\u3080\u6587\u66f8\u6570)\r\nTF-IDF(t, d) = TF(t, d) \u00d7 IDF(t)\r\n<\/pre>\n<p>\u3053\u308c\u306b\u3088\u308a\u3001\u300c\u7279\u5b9a\u306e\u6587\u66f8\u3067\u3088\u304f\u51fa\u73fe\u3059\u308b\u304c\u3001\u5168\u4f53\u3067\u306f\u73cd\u3057\u3044\u5358\u8a9e\u300d\u304c\u9ad8\u30b9\u30b3\u30a2\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<h4 id=\"rtoc-21\" >Python\u3067\u306e\u5b9f\u88c5\u4f8b<\/h4>\n<pre><code>from sklearn.feature_extraction.text import TfidfVectorizer\r\nimport MeCab\r\n\r\n# \u65e5\u672c\u8a9e\u5f62\u614b\u7d20\u89e3\u6790\r\ndef tokenize_ja(text):\r\n    mecab = MeCab.Tagger(\"-Owakati\")\r\n    return mecab.parse(text).strip().split()\r\n\r\n# TF-IDF\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\r\ndocuments = [\r\n    \"\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u306fAI\u6280\u8853\u306e\u91cd\u8981\u306a\u5206\u91ce\u3067\u3059\",\r\n    \"\u6a5f\u68b0\u5b66\u7fd2\u3068\u6df1\u5c64\u5b66\u7fd2\u304cNLP\u3092\u9032\u5316\u3055\u305b\u307e\u3057\u305f\",\r\n    \"\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306fSEO\u5bfe\u7b56\u306b\u6d3b\u7528\u3067\u304d\u307e\u3059\"\r\n]\r\n\r\nvectorizer = TfidfVectorizer(tokenizer=tokenize_ja)\r\ntfidf_matrix = vectorizer.fit_transform(documents)\r\nfeature_names = vectorizer.get_feature_names_out()\r\n\r\n# \u5404\u6587\u66f8\u306e\u30c8\u30c3\u30d7\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u8868\u793a\r\nfor i, doc in enumerate(documents):\r\n    scores = tfidf_matrix[i].toarray()[0]\r\n    top_indices = scores.argsort()[-5:][::-1]\r\n    keywords = [(feature_names[j], scores[j]) for j in top_indices]\r\n    print(f\"\u6587\u66f8{i+1}: {keywords}\")\r\n<\/code><\/pre>\n<h3 id=\"lda\">\u30c8\u30d4\u30c3\u30af\u30e2\u30c7\u30eb\uff08LDA\uff09<\/h3>\n<p><strong>LDA\uff08Latent Dirichlet Allocation\uff1a\u6f5c\u5728\u7684\u30c7\u30a3\u30ea\u30af\u30ec\u914d\u5206\u6cd5\uff09<\/strong>\u306f\u3001\u6587\u66f8\u96c6\u5408\u304b\u3089\u6f5c\u5728\u7684\u306a\u30c8\u30d4\u30c3\u30af\u3092\u767a\u898b\u3057\u3001\u5404\u30c8\u30d4\u30c3\u30af\u3092\u7279\u5fb4\u3065\u3051\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u62bd\u51fa\u3059\u308b\u624b\u6cd5\u3067\u3059\u3002<\/p>\n<h4 id=\"rtoc-23\" >LDA\u306e\u7279\u5fb4<\/h4>\n<ul>\n<li><strong>\u6559\u5e2b\u306a\u3057\u5b66\u7fd2<\/strong>\uff1a\u30e9\u30d9\u30eb\u306a\u3057\u30c7\u30fc\u30bf\u304b\u3089\u30c8\u30d4\u30c3\u30af\u3092\u81ea\u52d5\u767a\u898b<\/li>\n<li><strong>\u78ba\u7387\u7684\u30e2\u30c7\u30eb<\/strong>\uff1a\u5404\u6587\u66f8\u304c\u8907\u6570\u30c8\u30d4\u30c3\u30af\u306e\u6df7\u5408\u3068\u3057\u3066\u8868\u73fe\u3055\u308c\u308b<\/li>\n<li><strong>\u89e3\u91c8\u53ef\u80fd\u6027<\/strong>\uff1a\u5404\u30c8\u30d4\u30c3\u30af\u3092\u4ee3\u8868\u3059\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u30ea\u30b9\u30c8\u304c\u5f97\u3089\u308c\u308b<\/li>\n<\/ul>\n<h4 id=\"rtoc-24\" >LDA\u306e\u4ed5\u7d44\u307f<\/h4>\n<p>LDA\u306f\u4ee5\u4e0b\u306e\u4eee\u5b9a\u306b\u57fa\u3065\u3044\u3066\u3044\u307e\u3059\u3002<\/p>\n<ol>\n<li>\u5404\u6587\u66f8\u306f\u8907\u6570\u306e\u30c8\u30d4\u30c3\u30af\u306e\u6df7\u5408\u3067\u3042\u308b<\/li>\n<li>\u5404\u30c8\u30d4\u30c3\u30af\u306f\u5358\u8a9e\u306e\u78ba\u7387\u5206\u5e03\u3067\u3042\u308b<\/li>\n<li>\u6587\u66f8\u751f\u6210\u306f\u300c\u30c8\u30d4\u30c3\u30af\u9078\u629e\u300d\u2192\u300c\u5358\u8a9e\u9078\u629e\u300d\u306e2\u6bb5\u968e\u30d7\u30ed\u30bb\u30b9<\/li>\n<\/ol>\n<h4 id=\"rtoc-25\" >Python\u3067\u306e\u5b9f\u88c5\u4f8b<\/h4>\n<pre><code>from gensim import corpora\r\nfrom gensim.models import LdaModel\r\nimport MeCab\r\n\r\n# \u30c6\u30ad\u30b9\u30c8\u306e\u30c8\u30fc\u30af\u30f3\u5316\r\ndef tokenize(text):\r\n    mecab = MeCab.Tagger(\"-Owakati\")\r\n    return mecab.parse(text).strip().split()\r\n\r\ndocuments = [\r\n    \"\u6a5f\u68b0\u5b66\u7fd2\u306f\u4eba\u5de5\u77e5\u80fd\u306e\u4e00\u5206\u91ce\u3067\u3042\u308b\",\r\n    \"\u6df1\u5c64\u5b66\u7fd2\u306f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u4f7f\u7528\u3059\u308b\",\r\n    \"\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u306f\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u5206\u6790\u3059\u308b\u6280\u8853\u3060\"\r\n]\r\n\r\n# \u8f9e\u66f8\u3068\u30b3\u30fc\u30d1\u30b9\u306e\u4f5c\u6210\r\ntokenized_docs = [tokenize(doc) for doc in documents]\r\ndictionary = corpora.Dictionary(tokenized_docs)\r\ncorpus = [dictionary.doc2bow(doc) for doc in tokenized_docs]\r\n\r\n# LDA\u30e2\u30c7\u30eb\u306e\u5b66\u7fd2\r\nlda_model = LdaModel(corpus, num_topics=3, id2word=dictionary, passes=15)\r\n\r\n# \u30c8\u30d4\u30c3\u30af\u3054\u3068\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u8868\u793a\r\nfor idx, topic in lda_model.print_topics(-1):\r\n    print(f\"\u30c8\u30d4\u30c3\u30af {idx}: {topic}\")\r\n<\/code><\/pre>\n<h3 id=\"word2vec-bert\">\u5206\u6563\u8868\u73fe\uff08Word2Vec, BERT\uff09<\/h3>\n<p><strong>\u5206\u6563\u8868\u73fe\uff08Word Embeddings\uff09<\/strong>\u306f\u3001\u5358\u8a9e\u3092\u5bc6\u306a\u30d9\u30af\u30c8\u30eb\uff08\u901a\u5e38100\u301c768\u6b21\u5143\uff09\u3067\u8868\u73fe\u3059\u308b\u6280\u8853\u3067\u3059\u3002\u610f\u5473\u7684\u306b\u8fd1\u3044\u5358\u8a9e\u306f\u3001\u30d9\u30af\u30c8\u30eb\u7a7a\u9593\u4e0a\u3067\u3082\u8fd1\u304f\u306b\u914d\u7f6e\u3055\u308c\u307e\u3059\u3002<\/p>\n<h4 id=\"rtoc-27\" >Word2Vec\u306e\u7279\u5fb4<\/h4>\n<ul>\n<li><strong>CBOW<\/strong>\uff1a\u5468\u56f2\u306e\u5358\u8a9e\u304b\u3089\u4e2d\u5fc3\u306e\u5358\u8a9e\u3092\u4e88\u6e2c<\/li>\n<li><strong>Skip-gram<\/strong>\uff1a\u4e2d\u5fc3\u306e\u5358\u8a9e\u304b\u3089\u5468\u56f2\u306e\u5358\u8a9e\u3092\u4e88\u6e2c<\/li>\n<li><strong>\u610f\u5473\u7684\u985e\u4f3c\u6027<\/strong>\uff1a\u300c\u738b\u69d8 &#8211; \u7537\u6027 + \u5973\u6027 \u2248 \u5973\u738b\u300d\u306e\u3088\u3046\u306a\u6f14\u7b97\u304c\u53ef\u80fd<\/li>\n<\/ul>\n<h4 id=\"rtoc-28\" >BERT\u306e\u7279\u5fb4<\/h4>\n<ul>\n<li><strong>\u53cc\u65b9\u5411\u6587\u8108<\/strong>\uff1a\u524d\u5f8c\u4e21\u65b9\u306e\u6587\u8108\u3092\u540c\u6642\u306b\u8003\u616e<\/li>\n<li><strong>\u52d5\u7684\u57cb\u3081\u8fbc\u307f<\/strong>\uff1a\u540c\u3058\u5358\u8a9e\u3067\u3082\u6587\u8108\u306b\u3088\u3063\u3066\u7570\u306a\u308b\u30d9\u30af\u30c8\u30eb<\/li>\n<li><strong>\u4e8b\u524d\u5b66\u7fd2<\/strong>\uff1a\u5927\u898f\u6a21\u30b3\u30fc\u30d1\u30b9\u3067\u5b66\u7fd2\u6e08\u307f<\/li>\n<\/ul>\n<h4 id=\"rtoc-29\" >BERT\u3092\u4f7f\u3063\u305f\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\uff08KeyBERT\uff09<\/h4>\n<pre><code>from keybert import KeyBERT\r\nfrom sentence_transformers import SentenceTransformer\r\n\r\n# \u65e5\u672c\u8a9e\u5bfe\u5fdc\u30e2\u30c7\u30eb\u306e\u8aad\u307f\u8fbc\u307f\r\nmodel = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')\r\nkw_model = KeyBERT(model)\r\n\r\n# \u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\r\ndoc = \"\"\"\r\n\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u306f\u3001\u4eba\u9593\u304c\u65e5\u5e38\u7684\u306b\u4f7f\u7528\u3057\u3066\u3044\u308b\u8a00\u8a9e\u3092\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u306b\r\n\u7406\u89e3\u30fb\u751f\u6210\u3055\u305b\u308b\u4eba\u5de5\u77e5\u80fd\u6280\u8853\u306e\u4e00\u5206\u91ce\u3067\u3059\u3002\u6a5f\u68b0\u7ffb\u8a33\u3001\u611f\u60c5\u5206\u6790\u3001\r\n\u30c6\u30ad\u30b9\u30c8\u8981\u7d04\u3001\u8cea\u554f\u5fdc\u7b54\u30b7\u30b9\u30c6\u30e0\u306a\u3069\u5e45\u5e83\u3044\u5fdc\u7528\u304c\u3042\u308a\u307e\u3059\u3002\r\n\"\"\"\r\n\r\nkeywords = kw_model.extract_keywords(doc, keyphrase_ngram_range=(1, 2), top_n=5)\r\nprint(keywords)\r\n# \u51fa\u529b\u4f8b: [('\u81ea\u7136\u8a00\u8a9e\u51e6\u7406', 0.72), ('\u4eba\u5de5\u77e5\u80fd\u6280\u8853', 0.65), ('\u6a5f\u68b0\u7ffb\u8a33', 0.58), ...]\r\n<\/code><\/pre>\n<h3 id=\"cosine-similarity\">\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/h3>\n<p><strong>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/strong>\u306f\u30012\u3064\u306e\u30d9\u30af\u30c8\u30eb\u9593\u306e\u89d2\u5ea6\u306e\u4f59\u5f26\u5024\u3092\u4f7f\u3063\u3066\u985e\u4f3c\u6027\u3092\u6e2c\u5b9a\u3059\u308b\u624b\u6cd5\u3067\u3059\u3002\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3067\u306f\u3001\u6587\u66f8\u30d9\u30af\u30c8\u30eb\u3068\u5358\u8a9e\u30d9\u30af\u30c8\u30eb\u306e\u985e\u4f3c\u5ea6\u3092\u8a08\u7b97\u3057\u3066\u3001\u6587\u66f8\u3092\u4ee3\u8868\u3059\u308b\u5358\u8a9e\u3092\u7279\u5b9a\u3057\u307e\u3059\u3002<\/p>\n<h4 id=\"rtoc-31\" >\u8a08\u7b97\u5f0f<\/h4>\n<pre>\r\ncos(\u03b8) = (A \u00b7 B) \/ (||A|| \u00d7 ||B||)\r\n\r\nA \u00b7 B = \u03a3(Ai \u00d7 Bi)  \uff08\u5185\u7a4d\uff09\r\n||A|| = \u221a\u03a3(Ai\u00b2)     \uff08\u30ce\u30eb\u30e0\uff09\r\n<\/pre>\n<p>\u5024\u306f-1\u304b\u30891\u306e\u7bc4\u56f2\u3067\u30011\u306b\u8fd1\u3044\u307b\u3069\u985e\u4f3c\u5ea6\u304c\u9ad8\u3044\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002<\/p>\n<h4 id=\"rtoc-32\" >\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3078\u306e\u5fdc\u7528<\/h4>\n<ol>\n<li>\u6587\u66f8\u5168\u4f53\u306e\u30d9\u30af\u30c8\u30eb\uff08\u6587\u66f8\u57cb\u3081\u8fbc\u307f\uff09\u3092\u8a08\u7b97<\/li>\n<li>\u5404\u5358\u8a9e\u30fb\u30d5\u30ec\u30fc\u30ba\u306e\u30d9\u30af\u30c8\u30eb\u3092\u8a08\u7b97<\/li>\n<li>\u6587\u66f8\u30d9\u30af\u30c8\u30eb\u3068\u306e\u985e\u4f3c\u5ea6\u304c\u9ad8\u3044\u5358\u8a9e\u3092\u30ad\u30fc\u30ef\u30fc\u30c9\u3068\u3057\u3066\u62bd\u51fa<\/li>\n<\/ol>\n<h4 id=\"rtoc-33\" >\u5b9f\u88c5\u4f8b<\/h4>\n<pre><code>import numpy as np\r\nfrom sklearn.metrics.pairwise import cosine_similarity\r\nfrom sentence_transformers import SentenceTransformer\r\n\r\nmodel = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')\r\n\r\n# \u6587\u66f8\u3068\u5019\u88dc\u30ad\u30fc\u30ef\u30fc\u30c9\r\ndocument = \"\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306fSEO\u5bfe\u7b56\u306b\u6709\u52b9\u3067\u3059\"\r\ncandidates = [\"\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\", \"\u30ad\u30fc\u30ef\u30fc\u30c9\", \"SEO\", \"\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\", \"\u30c7\u30fc\u30bf\u5206\u6790\"]\r\n\r\n# \u30d9\u30af\u30c8\u30eb\u5316\r\ndoc_embedding = model.encode([document])\r\ncandidate_embeddings = model.encode(candidates)\r\n\r\n# \u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u8a08\u7b97\r\nsimilarities = cosine_similarity(doc_embedding, candidate_embeddings)[0]\r\n\r\n# \u30b9\u30b3\u30a2\u9806\u306b\u30bd\u30fc\u30c8\r\nresults = sorted(zip(candidates, similarities), key=lambda x: x[1], reverse=True)\r\nfor keyword, score in results:\r\n    print(f\"{keyword}: {score:.4f}\")\r\n<\/code><\/pre>\n<h3 id=\"gpt-extraction\">GPT\u306b\u3088\u308bNLP\u62bd\u51fa<\/h3>\n<p><strong>GPT\uff08Generative Pre-trained Transformer\uff09<\/strong>\u306a\u3069\u306e\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u306f\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306b\u3088\u308a\u67d4\u8edf\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u304c\u53ef\u80fd\u3067\u3059\u3002\u5f93\u6765\u624b\u6cd5\u3067\u306f\u96e3\u3057\u304b\u3063\u305f\u300c\u6587\u8108\u306b\u5fdc\u3058\u305f\u91cd\u8981\u8a9e\u306e\u5224\u65ad\u300d\u3092\u9ad8\u7cbe\u5ea6\u3067\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/p>\n<h4 id=\"rtoc-35\" >GPT\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u5229\u70b9<\/h4>\n<ul>\n<li><strong>\u30bc\u30ed\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2<\/strong>\uff1a\u8ffd\u52a0\u5b66\u7fd2\u306a\u3057\u3067\u5373\u5ea7\u306b\u5229\u7528\u53ef\u80fd<\/li>\n<li><strong>\u67d4\u8edf\u306a\u51fa\u529b\u5f62\u5f0f<\/strong>\uff1aJSON\u3001\u30ea\u30b9\u30c8\u3001\u8aac\u660e\u4ed8\u304d\u306a\u3069\u6307\u5b9a\u53ef\u80fd<\/li>\n<li><strong>\u30c9\u30e1\u30a4\u30f3\u9069\u5fdc<\/strong>\uff1a\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u4e0e\u3048\u308b\u3060\u3051\u3067\u5c02\u9580\u5206\u91ce\u306b\u5bfe\u5fdc<\/li>\n<li><strong>\u30de\u30eb\u30c1\u30bf\u30b9\u30af<\/strong>\uff1a\u62bd\u51fa\u3068\u540c\u6642\u306b\u5206\u985e\u3001\u8981\u7d04\u3082\u53ef\u80fd<\/li>\n<\/ul>\n<h4 id=\"rtoc-36\" >\u52b9\u679c\u7684\u306a\u30d7\u30ed\u30f3\u30d7\u30c8\u4f8b<\/h4>\n<pre><code># \u57fa\u672c\u7684\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u30d7\u30ed\u30f3\u30d7\u30c8\r\nprompt = \"\"\"\r\n\u4ee5\u4e0b\u306e\u30c6\u30ad\u30b9\u30c8\u304b\u3089\u3001\u4e3b\u8981\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u30925\u3064\u62bd\u51fa\u3057\u3066\u304f\u3060\u3055\u3044\u3002\r\n\u30ad\u30fc\u30ef\u30fc\u30c9\u306f\u91cd\u8981\u5ea6\u9806\u306b\u30ea\u30b9\u30c8\u30a2\u30c3\u30d7\u3057\u3001JSON\u5f62\u5f0f\u3067\u51fa\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\r\n\r\n\u30c6\u30ad\u30b9\u30c8:\r\n{text}\r\n\r\n\u51fa\u529b\u5f62\u5f0f:\r\n{\r\n  \"keywords\": [\r\n    {\"keyword\": \"\u30ad\u30fc\u30ef\u30fc\u30c91\", \"relevance\": \"\u9ad8\"},\r\n    {\"keyword\": \"\u30ad\u30fc\u30ef\u30fc\u30c92\", \"relevance\": \"\u4e2d\"},\r\n    ...\r\n  ]\r\n}\r\n\"\"\"\r\n\r\n# SEO\u5411\u3051\u30d7\u30ed\u30f3\u30d7\u30c8\r\nseo_prompt = \"\"\"\r\n\u4ee5\u4e0b\u306e\u30c6\u30ad\u30b9\u30c8\u3092\u5206\u6790\u3057\u3001SEO\u5bfe\u7b56\u306b\u6709\u52b9\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u62bd\u51fa\u3057\u3066\u304f\u3060\u3055\u3044\u3002\r\n\r\n\u62bd\u51fa\u6761\u4ef6:\r\n- \u691c\u7d22\u30dc\u30ea\u30e5\u30fc\u30e0\u304c\u671f\u5f85\u3067\u304d\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\r\n- \u30e1\u30a4\u30f3\u30ad\u30fc\u30ef\u30fc\u30c9\u3068\u95a2\u9023\u30ad\u30fc\u30ef\u30fc\u30c9\uff08LSI\uff09\u3092\u533a\u5225\r\n- 1\u301c3\u8a9e\u306e\u30d5\u30ec\u30fc\u30ba\u3092\u512a\u5148\r\n\r\n\u30c6\u30ad\u30b9\u30c8:\r\n{text}\r\n\"\"\"\r\n<\/code><\/pre>\n<h4 id=\"rtoc-37\" >Python\uff08OpenAI API\uff09\u3067\u306e\u5b9f\u88c5\u4f8b<\/h4>\n<pre><code>import openai\r\nimport json\r\n\r\ndef extract_keywords_gpt(text, num_keywords=5):\r\n    response = openai.ChatCompletion.create(\r\n        model=\"gpt-4\",\r\n        messages=[\r\n            {\"role\": \"system\", \"content\": \"\u3042\u306a\u305f\u306fSEO\u5c02\u9580\u5bb6\u3067\u3059\u3002\u30c6\u30ad\u30b9\u30c8\u304b\u3089\u91cd\u8981\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u62bd\u51fa\u3057\u3066\u304f\u3060\u3055\u3044\u3002\"},\r\n            {\"role\": \"user\", \"content\": f\"\"\"\r\n\u4ee5\u4e0b\u306e\u30c6\u30ad\u30b9\u30c8\u304b\u3089\u4e3b\u8981\u30ad\u30fc\u30ef\u30fc\u30c9\u3092{num_keywords}\u3064\u62bd\u51fa\u3057\u3001JSON\u5f62\u5f0f\u3067\u51fa\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002\r\n\r\n\u30c6\u30ad\u30b9\u30c8: {text}\r\n\r\n\u51fa\u529b\u5f62\u5f0f: {{\"keywords\": [\"\u30ad\u30fc\u30ef\u30fc\u30c91\", \"\u30ad\u30fc\u30ef\u30fc\u30c92\", ...]}}\r\n\"\"\"}\r\n        ],\r\n        temperature=0.3\r\n    )\r\n    result = json.loads(response.choices[0].message.content)\r\n    return result[\"keywords\"]\r\n\r\n# \u4f7f\u7528\u4f8b\r\ntext = \"\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306f\u3001SEO\u5bfe\u7b56\u3084\u30b3\u30f3\u30c6\u30f3\u30c4\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u306b\u6b20\u304b\u305b\u306a\u3044\u6280\u8853\u3067\u3059\"\r\nkeywords = extract_keywords_gpt(text)\r\nprint(keywords)\r\n<\/code><\/pre>\n<h3 id=\"classification-methods\">\u62bd\u51fa\u8a9e\u306e\u5206\u985e\u30fb\u6574\u7406\u65b9\u6cd5<\/h3>\n<p>\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u62bd\u51fa\u3057\u305f\u5f8c\u3001\u305d\u308c\u3089\u3092\u52b9\u679c\u7684\u306b\u6d3b\u7528\u3059\u308b\u305f\u3081\u306b\u306f\u9069\u5207\u306a\u5206\u985e\u30fb\u6574\u7406\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n<h4 id=\"rtoc-39\" >1. \u610f\u5473\u7684\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/h4>\n<p>\u985e\u4f3c\u3057\u305f\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u307e\u3059\u3002<\/p>\n<pre><code>from sklearn.cluster import KMeans\r\nfrom sentence_transformers import SentenceTransformer\r\n\r\n# \u30ad\u30fc\u30ef\u30fc\u30c9\u306e\u30d9\u30af\u30c8\u30eb\u5316\r\nmodel = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')\r\nkeywords = [\"SEO\", \"\u691c\u7d22\u30a8\u30f3\u30b8\u30f3\u6700\u9069\u5316\", \"\u30ad\u30fc\u30ef\u30fc\u30c9\", \"NLP\", \"\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\", \"\u6a5f\u68b0\u5b66\u7fd2\"]\r\nembeddings = model.encode(keywords)\r\n\r\n# \u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\r\nkmeans = KMeans(n_clusters=2, random_state=42)\r\nclusters = kmeans.fit_predict(embeddings)\r\n\r\n# \u7d50\u679c\u8868\u793a\r\nfor keyword, cluster in zip(keywords, clusters):\r\n    print(f\"{keyword}: \u30af\u30e9\u30b9\u30bf{cluster}\")\r\n<\/code><\/pre>\n<h4 id=\"rtoc-40\" >2. \u968e\u5c64\u7684\u5206\u985e<\/h4>\n<p>\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u968e\u5c64\u3067\u6574\u7406\u3057\u307e\u3059\u3002<\/p>\n<ul>\n<li><strong>\u30e1\u30a4\u30f3\u30ad\u30fc\u30ef\u30fc\u30c9<\/strong>\uff1a\u6587\u66f8\u306e\u4e3b\u984c\u3092\u8868\u3059\u6700\u91cd\u8981\u30ad\u30fc\u30ef\u30fc\u30c9<\/li>\n<li><strong>\u30b5\u30d6\u30ad\u30fc\u30ef\u30fc\u30c9<\/strong>\uff1a\u30e1\u30a4\u30f3\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u88dc\u8db3\u3059\u308b\u95a2\u9023\u8a9e<\/li>\n<li><strong>\u30ed\u30f3\u30b0\u30c6\u30fc\u30eb<\/strong>\uff1a\u3088\u308a\u5177\u4f53\u7684\u306a\u8907\u5408\u30ad\u30fc\u30ef\u30fc\u30c9<\/li>\n<li><strong>LSI\u30ad\u30fc\u30ef\u30fc\u30c9<\/strong>\uff1a\u6f5c\u5728\u7684\u610f\u5473\u3067\u95a2\u9023\u3059\u308b\u30ad\u30fc\u30ef\u30fc\u30c9<\/li>\n<\/ul>\n<h4 id=\"rtoc-41\" >3. \u54c1\u8a5e\u30fb\u30a8\u30f3\u30c6\u30a3\u30c6\u30a3\u306b\u3088\u308b\u5206\u985e<\/h4>\n<table>\n<thead>\n<tr>\n<th>\u5206\u985e<\/th>\n<th>\u4f8b<\/th>\n<th>\u6d3b\u7528\u30b7\u30fc\u30f3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u56fa\u6709\u540d\u8a5e<\/td>\n<td>Google, BERT, Python<\/td>\n<td>\u30d6\u30e9\u30f3\u30c9\u30fb\u88fd\u54c1\u5206\u6790<\/td>\n<\/tr>\n<tr>\n<td>\u666e\u901a\u540d\u8a5e<\/td>\n<td>\u30ad\u30fc\u30ef\u30fc\u30c9, \u62bd\u51fa, \u5206\u6790<\/td>\n<td>\u30c8\u30d4\u30c3\u30af\u5206\u6790<\/td>\n<\/tr>\n<tr>\n<td>\u52d5\u8a5e\u30fb\u5f62\u5bb9\u8a5e<\/td>\n<td>\u6700\u9069\u5316\u3059\u308b, \u52b9\u679c\u7684\u306a<\/td>\n<td>\u30a2\u30af\u30b7\u30e7\u30f3\u5206\u6790<\/td>\n<\/tr>\n<tr>\n<td>\u6570\u5024\u8868\u73fe<\/td>\n<td>2024\u5e74, 30%\u5411\u4e0a<\/td>\n<td>\u6642\u7cfb\u5217\u30fb\u5b9a\u91cf\u5206\u6790<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4 id=\"rtoc-42\" >4. \u611f\u60c5\u30fb\u610f\u56f3\u306b\u3088\u308b\u5206\u985e<\/h4>\n<ul>\n<li><strong>\u30dd\u30b8\u30c6\u30a3\u30d6<\/strong>\uff1a\u300c\u6210\u529f\u300d\u300c\u5411\u4e0a\u300d\u300c\u52b9\u679c\u7684\u300d<\/li>\n<li><strong>\u30cd\u30ac\u30c6\u30a3\u30d6<\/strong>\uff1a\u300c\u8ab2\u984c\u300d\u300c\u554f\u984c\u300d\u300c\u56f0\u96e3\u300d<\/li>\n<li><strong>\u30cb\u30e5\u30fc\u30c8\u30e9\u30eb<\/strong>\uff1a\u300c\u65b9\u6cd5\u300d\u300c\u624b\u9806\u300d\u300c\u6982\u8981\u300d<\/li>\n<li><strong>\u30a2\u30af\u30b7\u30e7\u30f3<\/strong>\uff1a\u300c\u5c0e\u5165\u300d\u300c\u5b9f\u88c5\u300d\u300c\u6539\u5584\u300d<\/li>\n<\/ul>\n<h2 id=\"seo-application\">NLP\u62bd\u51fa\u3092SEO\u306b\u3069\u3046\u6d3b\u304b\u3059\u304b\uff1f<\/h2>\n<p>NLP\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306f\u3001SEO\u5bfe\u7b56\u306b\u304a\u3044\u3066\u6975\u3081\u3066\u6709\u52b9\u306a\u30c4\u30fc\u30eb\u3067\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u5177\u4f53\u7684\u306a\u6d3b\u7528\u65b9\u6cd5\u3092\u89e3\u8aac\u3057\u307e\u3059\u3002<\/p>\n<h3 id=\"rtoc-44\" >1. \u30b3\u30f3\u30c6\u30f3\u30c4\u6700\u9069\u5316<\/h3>\n<h4 id=\"rtoc-45\" >\u30ad\u30fc\u30ef\u30fc\u30c9\u5bc6\u5ea6\u306e\u6700\u9069\u5316<\/h4>\n<p>\u62bd\u51fa\u3057\u305f\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u9069\u5207\u306a\u5bc6\u5ea6\u3067\u914d\u7f6e\u3059\u308b\u3053\u3068\u3067\u3001\u691c\u7d22\u30a8\u30f3\u30b8\u30f3\u306b\u30b3\u30f3\u30c6\u30f3\u30c4\u306e\u4e3b\u984c\u3092\u4f1d\u3048\u307e\u3059\u3002<\/p>\n<ul>\n<li><strong>\u30bf\u30a4\u30c8\u30eb\uff08H1\uff09<\/strong>\uff1a\u30e1\u30a4\u30f3\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u542b\u3081\u308b<\/li>\n<li><strong>\u898b\u51fa\u3057\uff08H2, H3\uff09<\/strong>\uff1a\u95a2\u9023\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u81ea\u7136\u306b\u914d\u7f6e<\/li>\n<li><strong>\u672c\u6587<\/strong>\uff1a1\u301c2%\u7a0b\u5ea6\u306e\u5bc6\u5ea6\u3092\u76ee\u5b89\u306b<\/li>\n<li><strong>\u30e1\u30bf\u30c7\u30a3\u30b9\u30af\u30ea\u30d7\u30b7\u30e7\u30f3<\/strong>\uff1a\u4e3b\u8981\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u542b\u3080120\u6587\u5b57\u7a0b\u5ea6<\/li>\n<\/ul>\n<h4 id=\"rtoc-46\" >LSI\u30ad\u30fc\u30ef\u30fc\u30c9\u306e\u6d3b\u7528<\/h4>\n<p>NLP\u3067\u62bd\u51fa\u3057\u305f\u95a2\u9023\u30ad\u30fc\u30ef\u30fc\u30c9\uff08LSI\uff1aLatent Semantic Indexing\uff09\u3092\u672c\u6587\u306b\u6563\u308a\u3070\u3081\u308b\u3053\u3068\u3067\u3001\u30b3\u30f3\u30c6\u30f3\u30c4\u306e\u7db2\u7f85\u6027\u3092\u9ad8\u3081\u307e\u3059\u3002<\/p>\n<pre><code># LSI\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u4f8b\r\nmain_keyword = \"\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\"\r\n# NLP\u3067\u62bd\u51fa\u3055\u308c\u308bLSI\u5019\u88dc\r\nlsi_keywords = [\r\n    \"\u6a5f\u68b0\u5b66\u7fd2\", \"AI\", \"\u30c6\u30ad\u30b9\u30c8\u30de\u30a4\u30cb\u30f3\u30b0\", \"\u5f62\u614b\u7d20\u89e3\u6790\",\r\n    \"BERT\", \"\u6df1\u5c64\u5b66\u7fd2\", \"\u30c1\u30e3\u30c3\u30c8\u30dc\u30c3\u30c8\", \"\u611f\u60c5\u5206\u6790\"\r\n]\r\n<\/code><\/pre>\n<h3 id=\"rtoc-47\" >2. \u7af6\u5408\u5206\u6790<\/h3>\n<p>\u7af6\u5408\u30b5\u30a4\u30c8\u306e\u30b3\u30f3\u30c6\u30f3\u30c4\u304b\u3089\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u62bd\u51fa\u3057\u3001\u81ea\u793e\u3068\u306e\u5dee\u5206\u3092\u5206\u6790\u3057\u307e\u3059\u3002<\/p>\n<pre><code>def analyze_competitors(competitor_contents):\r\n    \"\"\"\u7af6\u5408\u30b3\u30f3\u30c6\u30f3\u30c4\u304b\u3089\u30ad\u30fc\u30ef\u30fc\u30c9\u30ae\u30e3\u30c3\u30d7\u3092\u767a\u898b\"\"\"\r\n    all_keywords = set()\r\n    for content in competitor_contents:\r\n        keywords = extract_keywords(content)  # NLP\u62bd\u51fa\r\n        all_keywords.update(keywords)\r\n    \r\n    my_keywords = extract_keywords(my_content)\r\n    \r\n    # \u7af6\u5408\u306b\u3042\u3063\u3066\u81ea\u793e\u306b\u306a\u3044\u30ad\u30fc\u30ef\u30fc\u30c9\r\n    gaps = all_keywords - set(my_keywords)\r\n    return gaps\r\n<\/code><\/pre>\n<h3 id=\"rtoc-48\" >3. \u691c\u7d22\u610f\u56f3\u306e\u7406\u89e3<\/h3>\n<p>\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u5206\u985e\u3059\u308b\u3053\u3068\u3067\u3001\u30e6\u30fc\u30b6\u30fc\u306e\u691c\u7d22\u610f\u56f3\u3092\u628a\u63e1\u3057\u3001\u9069\u5207\u306a\u30b3\u30f3\u30c6\u30f3\u30c4\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/p>\n<table>\n<thead>\n<tr>\n<th>\u691c\u7d22\u610f\u56f3<\/th>\n<th>\u30ad\u30fc\u30ef\u30fc\u30c9\u4f8b<\/th>\n<th>\u6700\u9069\u30b3\u30f3\u30c6\u30f3\u30c4<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u60c5\u5831\u53ce\u96c6\uff08Know\uff09<\/td>\n<td>\u300c\u301c\u3068\u306f\u300d\u300c\u301c\u306e\u4ed5\u7d44\u307f\u300d<\/td>\n<td>\u89e3\u8aac\u8a18\u4e8b\u3001\u30ac\u30a4\u30c9<\/td>\n<\/tr>\n<tr>\n<td>\u3084\u308a\u65b9\uff08Do\uff09<\/td>\n<td>\u300c\u301c\u306e\u65b9\u6cd5\u300d\u300c\u301c\u306e\u3084\u308a\u65b9\u300d<\/td>\n<td>\u30cf\u30a6\u30c4\u30fc\u3001\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb<\/td>\n<\/tr>\n<tr>\n<td>\u6bd4\u8f03\uff08Compare\uff09<\/td>\n<td>\u300c\u301c vs \u301c\u300d\u300c\u6bd4\u8f03\u300d<\/td>\n<td>\u6bd4\u8f03\u8868\u3001\u30ec\u30d3\u30e5\u30fc<\/td>\n<\/tr>\n<tr>\n<td>\u8cfc\u5165\uff08Buy\uff09<\/td>\n<td>\u300c\u301c \u304a\u3059\u3059\u3081\u300d\u300c\u301c \u4fa1\u683c\u300d<\/td>\n<td>\u5546\u54c1\u30da\u30fc\u30b8\u3001\u30e9\u30f3\u30ad\u30f3\u30b0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3 id=\"rtoc-49\" >4. \u30b3\u30f3\u30c6\u30f3\u30c4\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/h3>\n<p>\u95a2\u9023\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u3001\u30d4\u30e9\u30fc\u30da\u30fc\u30b8\uff08\u4e3b\u8981\u8a18\u4e8b\uff09\u3068\u30af\u30e9\u30b9\u30bf\u30fc\u30b3\u30f3\u30c6\u30f3\u30c4\uff08\u95a2\u9023\u8a18\u4e8b\uff09\u3092\u8a2d\u8a08\u3057\u307e\u3059\u3002<\/p>\n<pre>\r\n\u3010\u30d4\u30e9\u30fc\u30da\u30fc\u30b8\u3011\r\n\u300c\u81ea\u7136\u8a00\u8a9e\u51e6\u7406 \u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u300d\r\n    \u2502\r\n    \u251c\u2500\u2500 \u3010\u30af\u30e9\u30b9\u30bf\u30fc1\u3011\u300cTF-IDF \u8a08\u7b97\u65b9\u6cd5\u300d\r\n    \u251c\u2500\u2500 \u3010\u30af\u30e9\u30b9\u30bf\u30fc2\u3011\u300cBERT \u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa Python\u300d\r\n    \u251c\u2500\u2500 \u3010\u30af\u30e9\u30b9\u30bf\u30fc3\u3011\u300cLDA \u30c8\u30d4\u30c3\u30af\u30e2\u30c7\u30eb \u4f7f\u3044\u65b9\u300d\r\n    \u251c\u2500\u2500 \u3010\u30af\u30e9\u30b9\u30bf\u30fc4\u3011\u300cGPT \u30ad\u30fc\u30ef\u30fc\u30c9\u5206\u6790 \u30d7\u30ed\u30f3\u30d7\u30c8\u300d\r\n    \u2514\u2500\u2500 \u3010\u30af\u30e9\u30b9\u30bf\u30fc5\u3011\u300cSEO \u30ad\u30fc\u30ef\u30fc\u30c9\u5bc6\u5ea6 \u6700\u9069\u5316\u300d\r\n<\/pre>\n<h3 id=\"rtoc-50\" >5. \u81ea\u52d5\u30bf\u30b0\u4ed8\u3051\u30fb\u30ab\u30c6\u30b4\u30ea\u5206\u985e<\/h3>\n<p>NLP\u3067\u30b3\u30f3\u30c6\u30f3\u30c4\u304b\u3089\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u81ea\u52d5\u62bd\u51fa\u3057\u3001\u30bf\u30b0\u3084\u30ab\u30c6\u30b4\u30ea\u3092\u81ea\u52d5\u4ed8\u4e0e\u3059\u308b\u3053\u3068\u3067\u3001\u30b5\u30a4\u30c8\u5185\u691c\u7d22\u3084\u30ca\u30d3\u30b2\u30fc\u30b7\u30e7\u30f3\u3092\u6539\u5584\u3057\u307e\u3059\u3002<\/p>\n<h3 id=\"rtoc-51\" >6. \u30c8\u30ec\u30f3\u30c9\u5206\u6790\u3068\u30b3\u30f3\u30c6\u30f3\u30c4\u4f01\u753b<\/h3>\n<p>SNS\u3084\u30cb\u30e5\u30fc\u30b9\u304b\u3089\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u7d99\u7d9a\u7684\u306b\u62bd\u51fa\u3057\u3001\u30c8\u30ec\u30f3\u30c9\u3092\u628a\u63e1\u3002\u30bf\u30a4\u30e0\u30ea\u30fc\u306a\u30b3\u30f3\u30c6\u30f3\u30c4\u4f01\u753b\u306b\u6d3b\u7528\u3057\u307e\u3059\u3002<\/p>\n<pre><code>def detect_trending_keywords(recent_texts, baseline_texts):\r\n    \"\"\"\u30c8\u30ec\u30f3\u30c9\u30ad\u30fc\u30ef\u30fc\u30c9\u306e\u691c\u51fa\"\"\"\r\n    recent_kw = extract_keywords_batch(recent_texts)\r\n    baseline_kw = extract_keywords_batch(baseline_texts)\r\n    \r\n    # \u51fa\u73fe\u983b\u5ea6\u306e\u5897\u52a0\u7387\u3067\u30bd\u30fc\u30c8\r\n    trending = []\r\n    for kw in recent_kw:\r\n        recent_freq = recent_kw.get(kw, 0)\r\n        baseline_freq = baseline_kw.get(kw, 0.1)  # \u30bc\u30ed\u9664\u7b97\u9632\u6b62\r\n        growth = recent_freq \/ baseline_freq\r\n        if growth > 1.5:  # 50%\u4ee5\u4e0a\u5897\u52a0\r\n            trending.append((kw, growth))\r\n    \r\n    return sorted(trending, key=lambda x: x[1], reverse=True)\r\n<\/code><\/pre>\n<h3 id=\"rtoc-52\" >\u5b9f\u8df5\u7684\u306aSEO\u30ef\u30fc\u30af\u30d5\u30ed\u30fc<\/h3>\n<p>NLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3092\u6d3b\u7528\u3057\u305fSEO\u30ef\u30fc\u30af\u30d5\u30ed\u30fc\u306e\u4f8b\u3067\u3059\u3002<\/p>\n<ol>\n<li><strong>\u30bf\u30fc\u30b2\u30c3\u30c8\u30ad\u30fc\u30ef\u30fc\u30c9\u9078\u5b9a<\/strong>\uff1a\u691c\u7d22\u30dc\u30ea\u30e5\u30fc\u30e0\u30fb\u7af6\u5408\u5ea6\u3092\u8abf\u67fb<\/li>\n<li><strong>\u7af6\u5408\u30b3\u30f3\u30c6\u30f3\u30c4\u5206\u6790<\/strong>\uff1a\u4e0a\u4f4d10\u30b5\u30a4\u30c8\u304b\u3089\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa<\/li>\n<li><strong>\u30ad\u30fc\u30ef\u30fc\u30c9\u30de\u30c3\u30d7\u4f5c\u6210<\/strong>\uff1a\u30e1\u30a4\u30f3\u3001\u30b5\u30d6\u3001LSI\u3092\u6574\u7406<\/li>\n<li><strong>\u30b3\u30f3\u30c6\u30f3\u30c4\u4f5c\u6210<\/strong>\uff1a\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u81ea\u7136\u306b\u914d\u7f6e\u3057\u305f\u8a18\u4e8b\u3092\u57f7\u7b46<\/li>\n<li><strong>\u516c\u958b\u5f8c\u5206\u6790<\/strong>\uff1a\u5b9f\u969b\u306e\u30e9\u30f3\u30ad\u30f3\u30b0\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u62bd\u51fa\u30fb\u6bd4\u8f03<\/li>\n<li><strong>\u7d99\u7d9a\u7684\u6700\u9069\u5316<\/strong>\uff1a\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u30c7\u30fc\u30bf\u3092\u57fa\u306b\u6539\u5584<\/li>\n<\/ol>\n<h2 id=\"rtoc-53\" >\u307e\u3068\u3081\uff1aNLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3067SEO\u3092\u5f37\u5316\u3057\u3088\u3046<\/h2>\n<p>\u672c\u8a18\u4e8b\u3067\u306f\u3001\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\uff08NLP\uff09\u306b\u3088\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306b\u3064\u3044\u3066\u3001\u57fa\u790e\u304b\u3089\u5fdc\u7528\u307e\u3067\u89e3\u8aac\u3057\u307e\u3057\u305f\u3002<\/p>\n<p><strong>\u62bc\u3055\u3048\u3066\u304a\u304d\u305f\u3044\u30dd\u30a4\u30f3\u30c8\uff1a<\/strong><\/p>\n<ul>\n<li>NLP\u306f\u5f93\u6765\u306e\u7d71\u8a08\u7684\u624b\u6cd5\u3068\u7570\u306a\u308a\u3001<strong>\u6587\u8108\u3092\u7406\u89e3\u3057\u305f<\/strong>\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u304c\u53ef\u80fd<\/li>\n<li><strong>TF-IDF\u3001LDA\u3001Word2Vec\u3001BERT\u3001GPT<\/strong>\u306a\u3069\u3001\u76ee\u7684\u306b\u5fdc\u3058\u305f\u624b\u6cd5\u3092\u9078\u629e<\/li>\n<li>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001<strong>\u6587\u66f8\u3092\u4ee3\u8868\u3059\u308b\u30ad\u30fc\u30ef\u30fc\u30c9<\/strong>\u3092\u7279\u5b9a\u3067\u304d\u308b<\/li>\n<li>\u62bd\u51fa\u3057\u305f\u30ad\u30fc\u30ef\u30fc\u30c9\u306f<strong>\u5206\u985e\u30fb\u6574\u7406<\/strong>\u3057\u3066\u6226\u7565\u7684\u306b\u6d3b\u7528<\/li>\n<li>SEO\u3067\u306f<strong>\u30b3\u30f3\u30c6\u30f3\u30c4\u6700\u9069\u5316\u3001\u7af6\u5408\u5206\u6790\u3001\u691c\u7d22\u610f\u56f3\u306e\u7406\u89e3<\/strong>\u306b\u5fdc\u7528<\/li>\n<\/ul>\n<p>AI\u306e\u9032\u5316\u306b\u3088\u308a\u3001\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306e\u7cbe\u5ea6\u306f\u4eca\u5f8c\u3055\u3089\u306b\u5411\u4e0a\u3057\u3066\u3044\u304f\u3067\u3057\u3087\u3046\u3002GPT\u3084BERT\u306a\u3069\u306e\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u3088\u308a\u9ad8\u5ea6\u306a\u5206\u6790\u304c\u53ef\u80fd\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u305c\u3072\u672c\u8a18\u4e8b\u3067\u7d39\u4ecb\u3057\u305f\u624b\u6cd5\u3092\u53c2\u8003\u306b\u3001\u81ea\u793e\u306eSEO\u5bfe\u7b56\u3084\u30b3\u30f3\u30c6\u30f3\u30c4\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u306b<strong>NLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa<\/strong>\u3092\u53d6\u308a\u5165\u308c\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p><!-- \u95a2\u9023\u8a18\u4e8b\u30ea\u30f3\u30af\uff08\u30b5\u30a4\u30c8\u306b\u5fdc\u3058\u3066\u8abf\u6574\uff09 --><\/p>\n<div class=\"related-posts\">\n<h3 id=\"rtoc-54\" >\u95a2\u9023\u8a18\u4e8b<\/h3>\n<ul>\n<li><a href=\"https:\/\/keywordfinder.jp\/blog\/yourself-seo\/\" title=\"\u81ea\u5206\u3067\u3067\u304d\u308bSEO\u5bfe\u7b56\u3068\u306f\uff1f\u521d\u5fc3\u8005\u3067\u3082\u4e0a\u4f4d\u8868\u793a\u3055\u305b\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u8003\u3048\u65b9\u307e\u3068\u3081\" rel=\"noopener\" target=\"_blank\">\u81ea\u5206\u3067\u3067\u304d\u308bSEO\u5bfe\u7b56\u3068\u306f\uff1f\u521d\u5fc3\u8005\u3067\u3082\u4e0a\u4f4d\u8868\u793a\u3055\u305b\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u8003\u3048\u65b9\u307e\u3068\u3081<\/a><\/li>\n<li><a href=\"https:\/\/keywordfinder.jp\/blog\/contentmarketing\/\" title=\"\u30b3\u30f3\u30c6\u30f3\u30c4\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u3068SEO\u306e\u9055\u3044\u3068\u306f\uff1f\u6df7\u5408\u3057\u3084\u3059\u3044\u5404\u65bd\u7b56\u3092\u8a73\u3057\u304f\u89e3\u8aac\u3057\u307e\u3059\" rel=\"noopener\" target=\"_blank\">\u30b3\u30f3\u30c6\u30f3\u30c4\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u3068SEO\u306e\u9055\u3044\u3068\u306f\uff1f\u6df7\u5408\u3057\u3084\u3059\u3044\u5404\u65bd\u7b56\u3092\u8a73\u3057\u304f\u89e3\u8aac\u3057\u307e\u3059<\/a><\/li>\n<\/ul>\n<\/div>\n<p><!-- FAQ\u69cb\u9020\u5316\u30c7\u30fc\u30bf\u7528 --><\/p>\n<h2 id=\"rtoc-55\" >\u3088\u304f\u3042\u308b\u8cea\u554f\uff08FAQ\uff09<\/h2>\n<div class=\"faq-item\">\n<h3 id=\"rtoc-56\" >Q. NLP\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u306f\u7121\u6599\u3067\u4f7f\u3048\u307e\u3059\u304b\uff1f<\/h3>\n<p>A. \u306f\u3044\u3001Python\u306eNLTK\u3001spaCy\u3001Gensim\u3001scikit-learn\u306a\u3069\u306e\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u3048\u3070\u7121\u6599\u3067\u5b9f\u88c5\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001Google Colab\u7b49\u306e\u7121\u6599\u74b0\u5883\u3067\u3082\u5b9f\u884c\u53ef\u80fd\u3067\u3059\u3002GPT-4\u306a\u3069\u306eAPI\u306f\u5f93\u91cf\u8ab2\u91d1\u3067\u3059\u304c\u3001\u5c11\u91cf\u306a\u3089\u4f4e\u30b3\u30b9\u30c8\u3067\u5229\u7528\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/div>\n<div class=\"faq-item\">\n<h3 id=\"rtoc-57\" >Q. \u65e5\u672c\u8a9e\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3067\u6ce8\u610f\u3059\u3079\u304d\u70b9\u306f\uff1f<\/h3>\n<p>A. \u65e5\u672c\u8a9e\u306f\u82f1\u8a9e\u3068\u7570\u306a\u308a\u3001\u5358\u8a9e\u9593\u306b\u30b9\u30da\u30fc\u30b9\u304c\u306a\u3044\u305f\u3081\u3001\u5f62\u614b\u7d20\u89e3\u6790\uff08MeCab\u3001Janome\u3001SudachiPy\u306a\u3069\uff09\u304c\u5fc5\u9808\u3067\u3059\u3002\u307e\u305f\u3001\u65e5\u672c\u8a9e\u5bfe\u5fdc\u306eBERT\u30e2\u30c7\u30eb\uff08\u6771\u5317\u5927\u5b66\u306ecl-tohoku\/bert-base-japanese\u7b49\uff09\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u7cbe\u5ea6\u304c\u5411\u4e0a\u3057\u307e\u3059\u3002<\/p>\n<\/div>\n<div class=\"faq-item\">\n<h3 id=\"rtoc-58\" >Q. \u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u7d50\u679c\u306e\u7cbe\u5ea6\u3092\u4e0a\u3052\u308b\u306b\u306f\uff1f<\/h3>\n<p>A. \u30c9\u30e1\u30a4\u30f3\u7279\u5316\u306e\u8f9e\u66f8\u3092\u8ffd\u52a0\u3059\u308b\u3001\u8907\u6570\u624b\u6cd5\u306e\u7d50\u679c\u3092\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u3059\u308b\u3001\u62bd\u51fa\u7d50\u679c\u3092\u4eba\u9593\u304c\u30ec\u30d3\u30e5\u30fc\u3057\u3066\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3059\u308b\u3001\u3068\u3044\u3063\u305f\u65b9\u6cd5\u304c\u52b9\u679c\u7684\u3067\u3059\u3002\u307e\u305f\u3001BERT\u3084GPT\u306a\u3069\u306e\u6700\u65b0\u30e2\u30c7\u30eb\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u6587\u8108\u7406\u89e3\u304c\u5411\u4e0a\u3057\u7cbe\u5ea6\u304c\u9ad8\u307e\u308a\u307e\u3059\u3002<\/p>\n<\/div>\n<div class=\"faq-item\">\n<h3 id=\"rtoc-59\" >Q. SEO\u3067\u30ad\u30fc\u30ef\u30fc\u30c9\u62bd\u51fa\u3092\u4f7f\u3046\u5177\u4f53\u7684\u306a\u30e1\u30ea\u30c3\u30c8\u306f\uff1f<\/h3>\n<p>A. \u7af6\u5408\u5206\u6790\u306e\u52b9\u7387\u5316\u3001LSI\u30ad\u30fc\u30ef\u30fc\u30c9\u306e\u767a\u898b\u3001\u30b3\u30f3\u30c6\u30f3\u30c4\u306e\u7db2\u7f85\u6027\u5411\u4e0a\u3001\u691c\u7d22\u610f\u56f3\u306e\u7406\u89e3\u6df1\u5316\u3001\u30bf\u30b0\u30fb\u30ab\u30c6\u30b4\u30ea\u306e\u81ea\u52d5\u4ed8\u4e0e\u306a\u3069\u3001\u591a\u304f\u306e\u30e1\u30ea\u30c3\u30c8\u304c\u3042\u308a\u307e\u3059\u3002\u624b\u4f5c\u696d\u3067\u306f\u898b\u843d\u3068\u3057\u304c\u3061\u306a\u95a2\u9023\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u6f0f\u308c\u306a\u304f\u62bd\u51fa\u3067\u304d\u308b\u70b9\u304c\u6700\u5927\u306e\u5229\u70b9\u3067\u3059\u3002<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u300c\u81a8\u5927\u306a\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u304b\u3089\u3001\u91cd\u8981\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u81ea\u52d5\u3067\u62bd\u51fa\u3057\u305f\u3044\u300d\u300cSEO\u5bfe\u7b56\u306e\u305f\u3081\u306b\u3001\u30b3\u30f3\u30c6\u30f3\u30c4\u306e\u4e3b\u984c\u3092\u7684\u78ba\u306b\u628a\u63e1\u3057\u305f\u3044\u300d\u2014\u2014\u305d\u3093\u306a\u8ab2\u984c\u3092\u62b1\u3048\u3066\u3044\u308b\u306a\u3089\u3001\u81ea\u7136\u8a00 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":12535,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-12530","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-seo"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.10 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u300c\u81a8\u5927\u306a\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u304b\u3089\u3001\u91cd\u8981\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u81ea\u52d5\u3067\u62bd\u51fa\u3057\u305f\u3044\u300d\u300cSEO\u5bfe\u7b56\u306e\u305f\u3081\u306b\u3001\u30b3\u30f3\u30c6\u30f3\u30c4\u306e\u4e3b\u984c\u3092\u7684\u78ba\u306b\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"\u30ad\u30fc\u30ef\u30fc\u30c9\u30d5\u30a1\u30a4\u30f3\u30c0\u30fc\u7de8\u96c6\u90e8\"\/>\n\t<link 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content=\"\u300c\u81a8\u5927\u306a\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u304b\u3089\u3001\u91cd\u8981\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u3092\u81ea\u52d5\u3067\u62bd\u51fa\u3057\u305f\u3044\u300d\u300cSEO\u5bfe\u7b56\u306e\u305f\u3081\u306b\u3001\u30b3\u30f3\u30c6\u30f3\u30c4\u306e\u4e3b\u984c\u3092\u7684\u78ba\u306b\" \/>\n\t\t<meta property=\"og:url\" content=\"https:\/\/keywordfinder.jp\/blog\/seo50_nlp_keyword_extraction\/\" \/>\n\t\t<meta property=\"og:image\" content=\"https:\/\/keywordfinder.jp\/blog\/wp-content\/uploads\/2025\/12\/7b8d327cd84c0d5fa63f454bc8b6562f.png\" \/>\n\t\t<meta property=\"og:image:secure_url\" content=\"https:\/\/keywordfinder.jp\/blog\/wp-content\/uploads\/2025\/12\/7b8d327cd84c0d5fa63f454bc8b6562f.png\" \/>\n\t\t<meta property=\"og:image:width\" content=\"589\" \/>\n\t\t<meta property=\"og:image:height\" content=\"427\" \/>\n\t\t<meta property=\"article:published_time\" content=\"2025-12-09T01:37:58+00:00\" \/>\n\t\t<meta 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