{"id":476792,"date":"2023-08-09T07:36:15","date_gmt":"2023-08-09T07:36:15","guid":{"rendered":""},"modified":"2023-09-05T11:13:27","modified_gmt":"2023-09-05T11:13:27","slug":"dependency-parsing","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/dependency-parsing\/","title":{"rendered":"\u4f9d\u5b58\u5206\u6790"},"content":{"rendered":"<p>\u4f9d\u5b58\u5206\u6790\u662f\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u9886\u57df\u4f7f\u7528\u7684\u4e00\u9879\u91cd\u8981\u6280\u672f\uff0c\u6709\u52a9\u4e8e\u7406\u89e3\u548c\u8868\u793a\u53e5\u5b50\u7684\u8bed\u6cd5\u7ed3\u6784\u3002\u5b83\u6784\u6210\u4e86 NLP \u4e2d\u591a\u4e2a\u5e94\u7528\u7a0b\u5e8f\u7684\u652f\u67f1\uff0c\u4f8b\u5982\u673a\u5668\u7ffb\u8bd1\u3001\u4fe1\u606f\u63d0\u53d6\u548c\u95ee\u7b54\u7cfb\u7edf\u3002<\/p>\n<h2>\u4f9d\u5b58\u53e5\u6cd5\u7684\u5386\u53f2\u80cc\u666f\u548c\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\u4f5c\u4e3a\u4e00\u4e2a\u6982\u5ff5\u8d77\u6e90\u4e8e\u7406\u8bba\u8bed\u8a00\u5b66\u7684\u65e9\u671f\u3002\u7b2c\u4e00\u4e2a\u6982\u5ff5\u7684\u7075\u611f\u6765\u81ea\u4e8e\u53e4\u5370\u5ea6\u8bed\u6cd5\u5b66\u5bb6\u5e15\u5c3c\u5c3c\u7684\u4f20\u7edf\u8bed\u6cd5\u7406\u8bba\u3002\u7136\u800c\uff0c\u73b0\u4ee3\u5f62\u5f0f\u7684\u4f9d\u5b58\u8bed\u6cd5\u4e3b\u8981\u662f\u7531\u8bed\u8a00\u5b66\u5bb6 Lucien Tesni\u00e8re \u5728 20 \u4e16\u7eaa\u5f00\u53d1\u7684\u3002<\/p>\n<p>Tesni\u00e8re \u5728\u5176 1959 \u5e74\u53bb\u4e16\u540e\u51fa\u7248\u7684\u5f00\u521b\u6027\u8457\u4f5c\u300a\u7ed3\u6784\u8bed\u6cd5\u5143\u7d20\u300b\u4e2d\u5f15\u5165\u4e86\u201c\u4f9d\u5b58\u201d\u4e00\u8bcd\u3002\u4ed6\u8ba4\u4e3a\uff0c\u4f7f\u7528\u4f9d\u5b58\u6982\u5ff5\u800c\u4e0d\u662f\u57fa\u4e8e\u9009\u533a\u7684\u65b9\u6cd5\u53ef\u4ee5\u6700\u597d\u5730\u6355\u6349\u5355\u8bcd\u4e4b\u95f4\u7684\u53e5\u6cd5\u5173\u7cfb\u3002<\/p>\n<h2>\u62d3\u5c55\u4e3b\u9898\uff1a\u4f9d\u5b58\u89e3\u6790\u8be6\u7ec6\u4fe1\u606f<\/h2>\n<p>\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\u7684\u76ee\u7684\u662f\u8bc6\u522b\u53e5\u5b50\u4e2d\u5355\u8bcd\u4e4b\u95f4\u7684\u8bed\u6cd5\u5173\u7cfb\uff0c\u5e76\u5c06\u5176\u8868\u793a\u4e3a\u6811\u7ed3\u6784\uff0c\u5176\u4e2d\u6bcf\u4e2a\u8282\u70b9\u4ee3\u8868\u4e00\u4e2a\u5355\u8bcd\uff0c\u6bcf\u6761\u8fb9\u4ee3\u8868\u5355\u8bcd\u4e4b\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb\u3002\u5728\u8fd9\u4e9b\u7ed3\u6784\u4e2d\uff0c\u4e00\u4e2a\u8bcd\uff08\u4e2d\u5fc3\u8bcd\uff09\u652f\u914d\u6216\u4f9d\u8d56\u4e8e\u5176\u4ed6\u8bcd\uff08\u4ece\u5c5e\u8bcd\uff09\u3002<\/p>\n<p>\u4f8b\u5982\uff0c\u8003\u8651\u8fd9\u53e5\u8bdd\uff1a\u201c\u7ea6\u7ff0\u6254\u4e86\u7403\u3002\u201d\u5728\u4f9d\u5b58\u5206\u6790\u6811\u4e2d\uff0c\u201cthrew\u201d\u5c06\u662f\u53e5\u5b50\u7684\u6839\uff08\u6216\u5934\uff09\uff0c\u800c\u201cJohn\u201d\u548c\u201cthe ball\u201d\u662f\u5176\u4ece\u5c5e\u8bcd\u3002\u6b64\u5916\uff0c\u201cthe ball\u201d\u53ef\u4ee5\u5206\u4e3a\u201cthe\u201d\u548c\u201cball\u201d\uff0c\u5176\u4e2d\u201cball\u201d\u662f\u4e2d\u5fc3\u8bcd\uff0c\u201cthe\u201d\u662f\u5176\u4ece\u5c5e\u8bcd\u3002<\/p>\n<h2>\u4f9d\u5b58\u89e3\u6790\u7684\u5185\u90e8\u7ed3\u6784\uff1a\u5b83\u662f\u5982\u4f55\u5de5\u4f5c\u7684<\/h2>\n<p>\u4f9d\u5b58\u89e3\u6790\u7531\u51e0\u4e2a\u9636\u6bb5\u7ec4\u6210\uff1a<\/p>\n<ol>\n<li><strong>\u4ee3\u5e01\u5316\uff1a<\/strong> \u6587\u672c\u88ab\u5206\u4e3a\u5355\u72ec\u7684\u5355\u8bcd\u6216\u6807\u8bb0\u3002<\/li>\n<li><strong>\u8bcd\u6027 (POS) \u6807\u8bb0\uff1a<\/strong> \u6bcf\u4e2a\u6807\u8bb0\u90fd\u6807\u6709\u5176\u9002\u5f53\u7684\u8bcd\u6027\uff0c\u4f8b\u5982\u540d\u8bcd\u3001\u52a8\u8bcd\u3001\u5f62\u5bb9\u8bcd\u7b49\u3002<\/li>\n<li><strong>\u4f9d\u8d56\u5173\u7cfb\u5206\u914d\uff1a<\/strong> \u57fa\u4e8e\u4f9d\u5b58\u8bed\u6cd5\u7684\u89c4\u5219\u5728\u6807\u8bb0\u4e4b\u95f4\u5206\u914d\u4f9d\u5b58\u5173\u7cfb\u3002\u4f8b\u5982\uff0c\u5728\u82f1\u8bed\u4e2d\uff0c\u52a8\u8bcd\u7684\u4e3b\u8bed\u901a\u5e38\u4f4d\u4e8e\u5176\u5de6\u4fa7\uff0c\u5bbe\u8bed\u4f4d\u4e8e\u5176\u53f3\u4fa7\u3002<\/li>\n<li><strong>\u6811\u7ed3\u6784\uff1a<\/strong> \u4ee5\u6807\u8bb0\u8bcd\u4f5c\u4e3a\u8282\u70b9\u3001\u4f9d\u8d56\u5173\u7cfb\u4f5c\u4e3a\u8fb9\u6784\u5efa\u89e3\u6790\u6811\u3002<\/li>\n<\/ol>\n<h2>\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\u7684\u4e3b\u8981\u7279\u70b9<\/h2>\n<p>\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\u7684\u57fa\u672c\u7279\u5f81\u5305\u62ec\uff1a<\/p>\n<ul>\n<li><strong>\u65b9\u5411\u6027\uff1a<\/strong> \u4f9d\u8d56\u5173\u7cfb\u672c\u8d28\u4e0a\u662f\u6709\u65b9\u5411\u7684\uff0c\u5373\u5b83\u4eec\u4ece\u5934\u6d41\u5411\u4f9d\u8d56\u5173\u7cfb\u3002<\/li>\n<li><strong>\u4e8c\u5143\u5173\u7cfb\uff1a<\/strong> \u6bcf\u4e2a\u4f9d\u8d56\u5173\u7cfb\u4ec5\u6d89\u53ca\u4e24\u4e2a\u5143\u7d20\uff1a\u5934\u5143\u7d20\u548c\u4ece\u5c5e\u5143\u7d20\u3002<\/li>\n<li><strong>\u7ed3\u6784\uff1a<\/strong> \u5b83\u521b\u5efa\u4e86\u4e00\u4e2a\u6811\u72b6\u7ed3\u6784\uff0c\u63d0\u4f9b\u4e86\u53e5\u5b50\u7684\u5206\u5c42\u89c6\u56fe\u3002<\/li>\n<li><strong>\u4f9d\u8d56\u7c7b\u578b\uff1a<\/strong> \u4e2d\u5fc3\u8bcd\u4e0e\u5176\u9644\u5c5e\u8bcd\u4e4b\u95f4\u7684\u5173\u7cfb\u88ab\u660e\u786e\u6807\u8bb0\u4e3a\u8bed\u6cd5\u5173\u7cfb\u7c7b\u578b\uff0c\u4f8b\u5982\u201c\u4e3b\u8bed\u201d\u3001\u201c\u5bbe\u8bed\u201d\u3001\u201c\u4fee\u9970\u8bed\u201d\u7b49\u3002<\/li>\n<\/ul>\n<h2>\u4f9d\u5b58\u5206\u6790\u7684\u7c7b\u578b<\/h2>\n<p>\u4f9d\u5b58\u5206\u6790\u65b9\u6cd5\u4e3b\u8981\u6709\u4e24\u79cd\u7c7b\u578b\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u57fa\u4e8e\u56fe\u7684\u6a21\u578b\uff1a<\/strong> \u8fd9\u4e9b\u6a21\u578b\u4e3a\u53e5\u5b50\u751f\u6210\u6240\u6709\u53ef\u80fd\u7684\u89e3\u6790\u6811\u5e76\u5bf9\u5b83\u4eec\u8fdb\u884c\u8bc4\u5206\u3002\u9009\u62e9\u5f97\u5206\u6700\u9ad8\u7684\u6811\u3002\u6700\u8457\u540d\u7684\u57fa\u4e8e\u56fe\u7684\u6a21\u578b\u662f\u827e\u65af\u7eb3\u7b97\u6cd5\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u57fa\u4e8e\u8f6c\u6362\u7684\u6a21\u578b\uff1a<\/strong> \u8fd9\u4e9b\u6a21\u578b\u9010\u6b65\u6784\u5efa\u89e3\u6790\u6811\u3002\u4ed6\u4eec\u4ece\u521d\u59cb\u914d\u7f6e\u5f00\u59cb\uff0c\u5e76\u5e94\u7528\u4e00\u7cfb\u5217\u64cd\u4f5c\uff08\u5982 SHIFT\u3001REDUCE\uff09\u6765\u6d3e\u751f\u89e3\u6790\u6811\u3002\u57fa\u4e8e\u8f6c\u6362\u7684\u6a21\u578b\u7684\u4e00\u4e2a\u793a\u4f8b\u662f Arc \u6807\u51c6\u7b97\u6cd5\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u4f7f\u7528\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\u7684\u65b9\u6cd5\u3001\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848<\/h2>\n<p>\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\u5e7f\u6cdb\u5e94\u7528\u4e8e NLP \u5e94\u7528\u4e2d\uff0c\u5305\u62ec\uff1a<\/p>\n<ul>\n<li><strong>\u673a\u5668\u7ffb\u8bd1\uff1a<\/strong> \u5b83\u6709\u52a9\u4e8e\u8bc6\u522b\u6e90\u8bed\u8a00\u4e2d\u7684\u8bed\u6cd5\u5173\u7cfb\u5e76\u5c06\u5176\u4fdd\u7559\u5728\u7ffb\u8bd1\u6587\u672c\u4e2d\u3002<\/li>\n<li><strong>\u4fe1\u606f\u63d0\u53d6\uff1a<\/strong> \u5b83\u6709\u52a9\u4e8e\u7406\u89e3\u6587\u672c\u7684\u542b\u4e49\u5e76\u63d0\u53d6\u6709\u7528\u7684\u4fe1\u606f\u3002<\/li>\n<li><strong>\u60c5\u7eea\u5206\u6790\uff1a<\/strong> \u901a\u8fc7\u8bc6\u522b\u4f9d\u8d56\u5173\u7cfb\uff0c\u53ef\u4ee5\u5e2e\u52a9\u66f4\u51c6\u786e\u5730\u7406\u89e3\u53e5\u5b50\u7684\u60c5\u611f\u3002<\/li>\n<\/ul>\n<p>\u7136\u800c\uff0c\u4f9d\u8d56\u89e3\u6790\u4e5f\u9762\u4e34\u7740\u6311\u6218\uff1a<\/p>\n<ul>\n<li><strong>\u6b67\u4e49\uff1a<\/strong> \u8bed\u8a00\u4e2d\u7684\u6b67\u4e49\u53ef\u80fd\u4f1a\u5bfc\u81f4\u4ea7\u751f\u591a\u4e2a\u6709\u6548\u7684\u89e3\u6790\u6811\u3002\u89e3\u51b3\u8fd9\u4e9b\u6b67\u4e49\u662f\u4e00\u9879\u5177\u6709\u6311\u6218\u6027\u7684\u4efb\u52a1\u3002<\/li>\n<li><strong>\u8868\u73b0\uff1a<\/strong> \u89e3\u6790\u53ef\u80fd\u9700\u8981\u5927\u91cf\u8ba1\u7b97\uff0c\u5c24\u5176\u662f\u5bf9\u4e8e\u957f\u53e5\u5b50\u3002<\/li>\n<\/ul>\n<p>\u89e3\u51b3\u529e\u6cd5\uff1a<\/p>\n<ul>\n<li><strong>\u673a\u5668\u5b66\u4e60\uff1a<\/strong> \u673a\u5668\u5b66\u4e60\u6280\u672f\u53ef\u7528\u4e8e\u6d88\u9664\u591a\u4e2a\u89e3\u6790\u6811\u4e4b\u95f4\u7684\u6b67\u4e49\u3002<\/li>\n<li><strong>\u4f18\u5316\u7b97\u6cd5\uff1a<\/strong> \u5df2\u7ecf\u5f00\u53d1\u51fa\u6709\u6548\u7684\u7b97\u6cd5\u6765\u4f18\u5316\u89e3\u6790\u8fc7\u7a0b\u3002<\/li>\n<\/ul>\n<h2>\u4e0e\u7c7b\u4f3c\u672f\u8bed\u7684\u6bd4\u8f83<\/h2>\n<table>\n<thead>\n<tr>\n<th><\/th>\n<th>\u4f9d\u5b58\u5206\u6790<\/th>\n<th>\u9009\u533a\u89e3\u6790<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u91cd\u70b9<\/td>\n<td>\u4e8c\u5143\u5173\u7cfb\uff08\u4f9d\u8d56\u5934\uff09<\/td>\n<td>\u77ed\u8bed\u6210\u5206<\/td>\n<\/tr>\n<tr>\n<td>\u7ed3\u6784<\/td>\n<td>\u6811\u72b6\u7ed3\u6784\uff0c\u6bcf\u4e2a\u5355\u8bcd\u53ef\u80fd\u6709\u4e00\u4e2a\u7236\u4ee3<\/td>\n<td>\u6811\u72b6\u7ed3\u6784\uff0c\u5141\u8bb8\u591a\u4e2a\u7236\u4ee3\u540c\u4e00\u4e2a\u8bcd<\/td>\n<\/tr>\n<tr>\n<td>\u7528\u4e8e<\/td>\n<td>\u4fe1\u606f\u62bd\u53d6\u3001\u673a\u5668\u7ffb\u8bd1\u3001\u60c5\u611f\u5206\u6790<\/td>\n<td>\u53e5\u5b50\u751f\u6210\u3001\u673a\u5668\u7ffb\u8bd1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e\u4f9d\u5b58\u53e5\u6cd5\u76f8\u5173\u7684\u672a\u6765\u5c55\u671b<\/h2>\n<p>\u968f\u7740\u673a\u5668\u5b66\u4e60\u548c\u4eba\u5de5\u667a\u80fd\u7684\u8fdb\u6b65\uff0c\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\u6709\u671b\u53d8\u5f97\u66f4\u52a0\u51c6\u786e\u548c\u9ad8\u6548\u3002 Transformer \u548c\u5faa\u73af\u795e\u7ecf\u7f51\u7edc (RNN) \u7b49\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\u6b63\u5728\u4e3a\u8be5\u9886\u57df\u505a\u51fa\u91cd\u5927\u8d21\u732e\u3002<\/p>\n<p>\u6b64\u5916\uff0c\u591a\u8bed\u8a00\u548c\u8de8\u8bed\u8a00\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\u662f\u4e00\u4e2a\u4e0d\u65ad\u589e\u957f\u7684\u7814\u7a76\u9886\u57df\u3002\u8fd9\u5c06\u4f7f\u7cfb\u7edf\u80fd\u591f\u7528\u66f4\u5c11\u7684\u8d44\u6e90\u6709\u6548\u5730\u7406\u89e3\u548c\u7ffb\u8bd1\u8bed\u8a00\u3002<\/p>\n<h2>\u4ee3\u7406\u670d\u52a1\u5668\u548c\u4f9d\u8d56\u89e3\u6790<\/h2>\n<p>\u867d\u7136\u4ee3\u7406\u670d\u52a1\u5668\u4e0d\u76f4\u63a5\u4e0e\u4f9d\u8d56\u9879\u89e3\u6790\u4ea4\u4e92\uff0c\u4f46\u5b83\u4eec\u53ef\u7528\u4e8e\u4fc3\u8fdb\u5229\u7528\u6b64\u6280\u672f\u7684 NLP \u4efb\u52a1\u3002\u4f8b\u5982\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u7528\u4e8e\u6293\u53d6 Web \u6570\u636e\u4ee5\u8bad\u7ec3 NLP \u6a21\u578b\uff0c\u5305\u62ec\u7528\u4e8e\u4f9d\u8d56\u9879\u89e3\u6790\u7684\u6a21\u578b\u3002\u5b83\u8fd8\u63d0\u4f9b\u4e86\u4e00\u5c42\u533f\u540d\u6027\uff0c\u4ece\u800c\u4fdd\u62a4\u4e86\u8fdb\u884c\u8fd9\u4e9b\u64cd\u4f5c\u7684\u4e2a\u4eba\u6216\u7ec4\u7ec7\u7684\u9690\u79c1\u3002<\/p>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<ol>\n<li><a href=\"https:\/\/nlp.stanford.edu\/pubs\/Dozat2017Dependency.pdf\" target=\"_new\" rel=\"noopener nofollow\">\u65af\u5766\u798f\u5927\u5b66\u7684\u901a\u7528\u4f9d\u5b58\u5206\u6790\u8bba\u6587<\/a><\/li>\n<li><a href=\"https:\/\/spacy.io\/api\/dependencyparser\" target=\"_new\" rel=\"noopener nofollow\">Spacy \u7684\u4f9d\u8d56\u89e3\u6790\u6587\u6863<\/a><\/li>\n<li><a href=\"https:\/\/www.sketchengine.eu\/user-guide\/user-manual\/corpora-by-languages\/dependency-grammar\/\" target=\"_new\" rel=\"noopener nofollow\">\u4f9d\u5b58\u8bed\u6cd5\u7b80\u4ecb<\/a><\/li>\n<li><a href=\"https:\/\/www.researchgate.net\/publication\/227988873_Lucien_Tesniere&#039;s_&#039;Elements_de_syntaxe_structurale&#039;_Fifty_years_on_1959-2009\" target=\"_new\" rel=\"noopener nofollow\">Lucien Tesni\u00e8re \u548c\u4f9d\u5b58\u8bed\u6cd5<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468201,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476792","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Dependency Parsing: An Informative Guide<\/mark>","faq_items":[{"question":"What is Dependency Parsing?","answer":"<p>Dependency Parsing is a technique used in Natural Language Processing (NLP) to understand and represent the grammatical structure of a sentence. It forms the core of various applications in NLP, such as machine translation, information extraction, and question-answering systems.<\/p>"},{"question":"Who introduced the concept of Dependency Parsing?","answer":"<p>The concept of Dependency Parsing was introduced by Lucien Tesni\u00e8re in his work \"Elements of Structural Syntax,\" published in 1959. The idea originates from traditional grammatical theories, with its modern form developed by Tesni\u00e8re in the 20th century.<\/p>"},{"question":"What is the process of Dependency Parsing?","answer":"<p>Dependency Parsing involves several stages: Tokenization (dividing the text into individual words), Part-of-Speech (POS) Tagging (labeling each word with its part of speech), Dependency Relation Assignment (assigning a dependency relation between words based on the rules of dependency grammar), and Tree Construction (constructing a parse tree with words as nodes and dependency relations as edges).<\/p>"},{"question":"What are the key features of Dependency Parsing?","answer":"<p>Key features of Dependency Parsing include directionality (dependency relations are directional), binary relations (each dependency relation involves only two elements), a tree-like structure, and explicit labeling of dependency types (the relation between the head and its dependents is explicitly labeled with grammatical relation types).<\/p>"},{"question":"What are the different types of Dependency Parsing methods?","answer":"<p>There are primarily two types of Dependency Parsing methods: Graph-Based Models, which generate and score all possible parse trees for a sentence, and Transition-Based Models, which build parse trees incrementally, applying a sequence of actions to derive a parse tree.<\/p>"},{"question":"How is Dependency Parsing used?","answer":"<p>Dependency Parsing is used in several NLP applications like machine translation, where it helps in identifying grammatical relations in the source language, information extraction, where it aids in understanding the meaning of the text, and sentiment analysis, where it helps understand the sentiment of a sentence more accurately.<\/p>"},{"question":"How do Proxy Servers relate to Dependency Parsing?","answer":"<p>While proxy servers don't directly interact with Dependency Parsing, they can be used to facilitate NLP tasks that use this technique. For instance, a proxy server can be used to scrape web data for training NLP models, including those for Dependency Parsing, providing a layer of anonymity that protects the privacy of the individuals or organizations conducting these operations.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476792","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476792\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468201"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=476792"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}