{"id":932366,"date":"2024-12-26T17:56:50","date_gmt":"2024-12-26T09:56:50","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/932366.html"},"modified":"2024-12-26T17:56:53","modified_gmt":"2024-12-26T09:56:53","slug":"python%e5%a6%82%e4%bd%95%e6%9e%84%e5%bb%balda","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/932366.html","title":{"rendered":"python\u5982\u4f55\u6784\u5efalda"},"content":{"rendered":"

\"python\u5982\u4f55\u6784\u5efalda\"<\/p>\n

Python\u6784\u5efaLDA\u7684\u65b9\u6cd5\u6709\u591a\u79cd\u3001\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662f\u4f7f\u7528Gensim\u5e93\u3001LDA\u662f\u4e00\u79cd\u4e3b\u9898\u6a21\u578b\uff0c\u7528\u4e8e\u4ece\u6587\u672c\u6570\u636e\u4e2d\u53d1\u73b0\u4e3b\u9898\u3001\u901a\u8fc7LDA\u6a21\u578b\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u6587\u672c\u6570\u636e\u8fdb\u884c\u4e3b\u9898\u5206\u6790\uff0c\u5e2e\u52a9\u6211\u4eec\u7406\u89e3\u6587\u672c\u7684\u4e3b\u8981\u5185\u5bb9\u3002<\/strong><\/p>\n<\/p>\n

\u6784\u5efaLDA\u6a21\u578b\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\uff1a\u6587\u672c\u9884\u5904\u7406\u3001\u521b\u5efa\u8bcd\u5178\u548c\u8bed\u6599\u5e93\u3001\u8bad\u7ec3LDA\u6a21\u578b\u3001\u63d0\u53d6\u4e3b\u9898\u3002\u5728\u8fd9\u51e0\u4e2a\u6b65\u9aa4\u4e2d\uff0c\u6587\u672c\u9884\u5904\u7406\u975e\u5e38\u5173\u952e\uff0c\u56e0\u4e3a\u5b83\u76f4\u63a5\u5f71\u54cd\u6a21\u578b\u7684\u6548\u679c\u3002\u6211\u4eec\u901a\u5e38\u9700\u8981\u5bf9\u6587\u672c\u8fdb\u884c\u5206\u8bcd\u3001\u53bb\u9664\u505c\u7528\u8bcd\u3001\u8bcd\u5e72\u63d0\u53d6\u7b49\u5904\u7406\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u6784\u5efaLDA\u6a21\u578b\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6587\u672c\u9884\u5904\u7406<\/p>\n<\/p>\n

\u5728\u6784\u5efaLDA\u6a21\u578b\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5bf9\u6587\u672c\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002\u9884\u5904\u7406\u7684\u76ee\u7684\u662f\u4e3a\u4e86\u53bb\u9664\u566a\u58f0\uff0c\u63d0\u53d6\u6709\u6548\u4fe1\u606f\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n

    \n
  1. \u5206\u8bcd<\/li>\n<\/ol>\n

    \u5206\u8bcd\u662f\u6587\u672c\u9884\u5904\u7406\u7684\u7b2c\u4e00\u6b65\u3002\u6211\u4eec\u9700\u8981\u5c06\u6587\u672c\u5207\u5206\u4e3a\u4e00\u4e2a\u4e2a\u7684\u5355\u8bcd\uff0c\u4ee5\u4fbf\u540e\u7eed\u5904\u7406\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528jieba\u3001NLTK\u7b49\u5e93\u8fdb\u884c\u5206\u8bcd\u3002<\/p>\n<\/p>\n

    import jieba<\/p>\n
    text = "Python\u6784\u5efaLDA\u6a21\u578b\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\u6587\u672c\u9884\u5904\u7406\u3002"<\/p>\n
    words = jieba.lcut(text)<\/p>\n
    print(words)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u53bb\u9664\u505c\u7528\u8bcd<\/li>\n<\/ol>\n
      \u505c\u7528\u8bcd\u662f\u6307\u5728\u6587\u672c\u4e2d\u51fa\u73b0\u9891\u7387\u5f88\u9ad8\uff0c\u4f46\u5bf9\u8bed\u4e49\u8d21\u732e\u4e0d\u5927\u7684\u8bcd\uff0c\u5982\u201c\u7684\u201d\u3001\u201c\u4e86\u201d\u3001\u201c\u5728\u201d\u7b49\u3002\u5728\u8fdb\u884c\u4e3b\u9898\u5efa\u6a21\u65f6\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u53bb\u9664\u8fd9\u4e9b\u505c\u7528\u8bcd\u3002\u53ef\u4ee5\u4f7f\u7528NLTK\u63d0\u4f9b\u7684\u505c\u7528\u8bcd\u8868\uff0c\u6216\u8005\u81ea\u5b9a\u4e49\u505c\u7528\u8bcd\u8868\u3002<\/p>\n<\/p>\n
      from nltk.corpus import stopwords<\/p>\n
      stop_words = set(stopwords.words('chinese'))<\/p>\n
      filtered_words = [word for word in words if word not in stop_words]<\/p>\n
      print(filtered_words)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u8bcd\u5e72\u63d0\u53d6<\/li>\n<\/ol>\n
        \u8bcd\u5e72\u63d0\u53d6\u662f\u6307\u5c06\u5355\u8bcd\u8fd8\u539f\u4e3a\u5b83\u7684\u8bcd\u6839\u5f62\u5f0f\uff0c\u4ee5\u51cf\u5c11\u8bcd\u6c47\u8868\u7684\u5927\u5c0f\u3002\u5728\u82f1\u6587\u5904\u7406\u4e2d\uff0c\u901a\u5e38\u4f7f\u7528Porter Stemmer\u6216Lancaster Stemmer\u8fdb\u884c\u8bcd\u5e72\u63d0\u53d6\u3002<\/p>\n<\/p>\n
        from nltk.stem import PorterStemmer<\/p>\n
        ps = PorterStemmer()<\/p>\n
        stemmed_words = [ps.stem(word) for word in filtered_words]<\/p>\n
        print(stemmed_words)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e8c\u3001\u521b\u5efa\u8bcd\u5178\u548c\u8bed\u6599\u5e93<\/p>\n<\/p>\n
        \u5728\u5b8c\u6210\u6587\u672c\u9884\u5904\u7406\u540e\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u8bcd\u5178\u548c\u8bed\u6599\u5e93\uff0c\u4ee5\u4fbf\u8bad\u7ec3LDA\u6a21\u578b\u3002\u8bcd\u5178\u662f\u6307\u6240\u6709\u5355\u8bcd\u53ca\u5176ID\u7684\u6620\u5c04\uff0c\u8bed\u6599\u5e93\u662f\u6307\u6bcf\u4e2a\u6587\u6863\u4e2d\u5355\u8bcd\u7684ID\u53ca\u5176\u51fa\u73b0\u6b21\u6570\u3002<\/p>\n<\/p>\n
        from gensim import corpora<\/p>\n
        \u521b\u5efa\u8bcd\u5178<\/strong><\/h2>\n
        dictionary = corpora.Dictionary([stemmed_words])<\/p>\n
        \u521b\u5efa\u8bed\u6599\u5e93<\/strong><\/h2>\n
        corpus = [dictionary.doc2bow(stemmed_words)]<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e09\u3001\u8bad\u7ec3LDA\u6a21\u578b<\/p>\n<\/p>\n
        \u6709\u4e86\u8bcd\u5178\u548c\u8bed\u6599\u5e93\u540e\uff0c\u5c31\u53ef\u4ee5\u5f00\u59cb\u8bad\u7ec3LDA\u6a21\u578b\u4e86\u3002LDA\u6a21\u578b\u53ef\u4ee5\u4f7f\u7528Gensim\u5e93\u4e2d\u7684LdaModel<\/code>\u7c7b\u8fdb\u884c\u8bad\u7ec3\u3002<\/p>\n<\/p>\n
        from gensim.models import LdaModel<\/p>\n
        \u8bad\u7ec3LDA\u6a21\u578b<\/strong><\/h2>\n
        lda_model = LdaModel(corpus, num_topics=3, id2word=dictionary, passes=15)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u56db\u3001\u63d0\u53d6\u4e3b\u9898<\/p>\n<\/p>\n
        \u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4eceLDA\u6a21\u578b\u4e2d\u63d0\u53d6\u4e3b\u9898\u3002\u6bcf\u4e2a\u4e3b\u9898\u7531\u4e00\u7ec4\u5355\u8bcd\u53ca\u5176\u5bf9\u5e94\u7684\u6982\u7387\u7ec4\u6210\uff0c\u8fd9\u4e9b\u5355\u8bcd\u662f\u8fd9\u4e2a\u4e3b\u9898\u7684\u4ee3\u8868\u3002<\/p>\n<\/p>\n
        # \u6253\u5370\u6bcf\u4e2a\u4e3b\u9898\u7684\u8bcd\u6c47<\/p>\n
        for idx, topic in lda_model.print_topics(-1):<\/p>\n
            print(f"\u4e3b\u9898: {idx}\\n\u5355\u8bcd: {topic}")<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e94\u3001\u6a21\u578b\u4f18\u5316<\/p>\n<\/p>\n
        \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5bf9\u6a21\u578b\u8fdb\u884c\u4f18\u5316\uff0c\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u4e3b\u9898\u8bc6\u522b\u6548\u679c\u3002\u5e38\u89c1\u7684\u4f18\u5316\u65b9\u6cd5\u5305\u62ec\u8c03\u6574\u4e3b\u9898\u6570\u91cf\u3001\u589e\u52a0\u8bad\u7ec3\u8fed\u4ee3\u6b21\u6570\u3001\u4f18\u5316\u9884\u5904\u7406\u6b65\u9aa4\u7b49\u3002<\/p>\n<\/p>\n
          \n
        1. \u8c03\u6574\u4e3b\u9898\u6570\u91cf<\/li>\n<\/ol>\n
          \u9009\u62e9\u5408\u9002\u7684\u4e3b\u9898\u6570\u91cf\u662fLDA\u6a21\u578b\u4f18\u5316\u7684\u91cd\u8981\u4e00\u6b65\u3002\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u4e3b\u9898\u4e00\u81f4\u6027\u3001\u4e3b\u9898\u56f0\u60d1\u5ea6\u7b49\u6307\u6807\uff0c\u5e2e\u52a9\u786e\u5b9a\u5408\u9002\u7684\u4e3b\u9898\u6570\u91cf\u3002<\/p>\n<\/p>\n
          # \u8ba1\u7b97\u4e3b\u9898\u56f0\u60d1\u5ea6<\/p>\n
          perplexity = lda_model.log_perplexity(corpus)<\/p>\n
          print(f"\u56f0\u60d1\u5ea6: {perplexity}")<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u589e\u52a0\u8bad\u7ec3\u8fed\u4ee3\u6b21\u6570<\/li>\n<\/ol>\n
            \u589e\u52a0\u8bad\u7ec3\u8fed\u4ee3\u6b21\u6570\u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u6536\u655b\uff0c\u901a\u5e38\u53ef\u4ee5\u901a\u8fc7\u589e\u52a0passes<\/code>\u53c2\u6570\u7684\u503c\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
            # \u589e\u52a0\u8bad\u7ec3\u8fed\u4ee3\u6b21\u6570<\/p>\n
            lda_model = LdaModel(corpus, num_topics=3, id2word=dictionary, passes=50)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u4f18\u5316\u9884\u5904\u7406\u6b65\u9aa4<\/li>\n<\/ol>\n
              \u5728\u9884\u5904\u7406\u9636\u6bb5\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4e0d\u540c\u7684\u5206\u8bcd\u7b97\u6cd5\u3001\u505c\u7528\u8bcd\u8868\u3001\u8bcd\u5e72\u63d0\u53d6\u65b9\u6cd5\u7b49\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
              \u516d\u3001LDA\u6a21\u578b\u7684\u5e94\u7528<\/p>\n<\/p>\n
              LDA\u6a21\u578b\u53ef\u4ee5\u7528\u4e8e\u591a\u4e2a\u9886\u57df\u7684\u4e3b\u9898\u5206\u6790\uff0c\u5305\u62ec\u65b0\u95fb\u5206\u7c7b\u3001\u8206\u60c5\u76d1\u63a7\u3001\u5b66\u672f\u7814\u7a76\u7b49\u3002\u901a\u8fc7\u63d0\u53d6\u6587\u672c\u4e2d\u7684\u4e3b\u9898\uff0c\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u6570\u636e\u3002<\/p>\n<\/p>\n
                \n
              1. \u65b0\u95fb\u5206\u7c7b<\/li>\n<\/ol>\n
                \u901a\u8fc7LDA\u6a21\u578b\u5bf9\u65b0\u95fb\u6587\u672c\u8fdb\u884c\u4e3b\u9898\u5206\u6790\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u65b0\u95fb\u7684\u81ea\u52a8\u5206\u7c7b\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u5c06\u65b0\u95fb\u5206\u4e3a\u4f53\u80b2\u3001\u653f\u6cbb\u3001\u79d1\u6280\u7b49\u4e0d\u540c\u7c7b\u522b\u3002<\/p>\n<\/p>\n
                  \n
                1. \u8206\u60c5\u76d1\u63a7<\/li>\n<\/ol>\n
                  \u5728\u793e\u4ea4\u5a92\u4f53\u4e0a\uff0cLDA\u6a21\u578b\u53ef\u4ee5\u7528\u4e8e\u76d1\u63a7\u516c\u4f17\u5bf9\u67d0\u4e2a\u4e8b\u4ef6\u7684\u6001\u5ea6\u548c\u770b\u6cd5\u3002\u901a\u8fc7\u5206\u6790\u7528\u6237\u7684\u8bc4\u8bba\uff0c\u63d0\u53d6\u51fa\u4e3b\u9898\uff0c\u5e2e\u52a9\u4f01\u4e1a\u6216\u653f\u5e9c\u505a\u51fa\u76f8\u5e94\u7684\u51b3\u7b56\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u5b66\u672f\u7814\u7a76<\/li>\n<\/ol>\n
                    \u5728\u5b66\u672f\u7814\u7a76\u4e2d\uff0cLDA\u6a21\u578b\u53ef\u4ee5\u7528\u4e8e\u5206\u6790\u6587\u732e\u7684\u4e3b\u9898\u5206\u5e03\uff0c\u5e2e\u52a9\u7814\u7a76\u8005\u5feb\u901f\u4e86\u89e3\u67d0\u4e2a\u9886\u57df\u7684\u7814\u7a76\u70ed\u70b9\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
                    \u4e03\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                    \u6784\u5efaLDA\u6a21\u578b\u9700\u8981\u7ecf\u8fc7\u6587\u672c\u9884\u5904\u7406\u3001\u521b\u5efa\u8bcd\u5178\u548c\u8bed\u6599\u5e93\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u63d0\u53d6\u4e3b\u9898\u7b49\u6b65\u9aa4\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u548c\u4f18\u5316\u9884\u5904\u7406\u6b65\u9aa4\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u6548\u679c\u3002LDA\u6a21\u578b\u5728\u65b0\u95fb\u5206\u7c7b\u3001\u8206\u60c5\u76d1\u63a7\u3001\u5b66\u672f\u7814\u7a76\u7b49\u9886\u57df\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u524d\u666f\u3002\u901a\u8fc7\u5bf9\u6587\u672c\u6570\u636e\u7684\u4e3b\u9898\u5206\u6790\uff0c\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5229\u7528\u6570\u636e\u3002<\/p>\n<\/p>\n
                    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                     \u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0LDA\u6a21\u578b\uff1f<\/strong>
                    \u5728Python\u4e2d\uff0c\u6784\u5efaLDA\uff08Latent Dirichlet Allocation\uff09\u6a21\u578b\u901a\u5e38\u4f7f\u7528\u5e93\u5982Gensim\u548cScikit-learn\u3002\u9996\u5148\uff0c\u51c6\u5907\u6587\u672c\u6570\u636e\uff0c\u5c06\u5176\u6e05\u6d17\u548c\u9884\u5904\u7406\uff0c\u5305\u62ec\u53bb\u9664\u505c\u7528\u8bcd\u3001\u6807\u70b9\u7b26\u53f7\u4ee5\u53ca\u8fdb\u884c\u8bcd\u5e72\u5316\u6216\u8bcd\u5f62\u8fd8\u539f\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528Gensim\u7684Dictionary<\/code>\u548ccorpus<\/code>\u6765\u521b\u5efa\u8bcd\u6c47\u548c\u6587\u6863\u7684\u7a00\u758f\u8868\u793a\u3002\u6700\u540e\uff0c\u8c03\u7528models.LdaModel<\/code>\u6765\u8bad\u7ec3LDA\u6a21\u578b\uff0c\u5e76\u8c03\u6574\u8d85\u53c2\u6570\u4ee5\u4f18\u5316\u4e3b\u9898\u7684\u8d28\u91cf\u3002<\/p>\n
                    LDA\u6a21\u578b\u9002\u7528\u4e8e\u54ea\u4e9b\u7c7b\u578b\u7684\u6570\u636e\uff1f<\/strong>
                    LDA\u6a21\u578b\u7279\u522b\u9002\u5408\u5904\u7406\u6587\u672c\u6570\u636e\uff0c\u5c24\u5176\u662f\u5927\u89c4\u6a21\u6587\u6863\u96c6\u3002\u5b83\u80fd\u591f\u4ece\u6587\u6863\u4e2d\u81ea\u52a8\u63d0\u53d6\u4e3b\u9898\uff0c\u4f7f\u5176\u5728\u65b0\u95fb\u6587\u7ae0\u3001\u793e\u4ea4\u5a92\u4f53\u5e16\u5b50\u3001\u4ea7\u54c1\u8bc4\u8bba\u7b49\u9886\u57df\u8868\u73b0\u51fa\u8272\u3002\u901a\u8fc7\u5206\u6790\u5355\u8bcd\u7684\u5171\u73b0\u5173\u7cfb\uff0cLDA\u80fd\u591f\u6355\u6349\u5230\u6f5c\u5728\u7684\u4e3b\u9898\u7ed3\u6784\uff0c\u56e0\u6b64\u5bf9\u4e8e\u6587\u672c\u5206\u7c7b\u3001\u63a8\u8350\u7cfb\u7edf\u548c\u4fe1\u606f\u68c0\u7d22\u7b49\u5e94\u7528\u5177\u6709\u91cd\u8981\u4ef7\u503c\u3002<\/p>\n
                    \u5728\u6784\u5efaLDA\u6a21\u578b\u65f6\uff0c\u5982\u4f55\u9009\u62e9\u4e3b\u9898\u6570\u91cf\uff1f<\/strong>
                    \u9009\u62e9\u9002\u5f53\u7684\u4e3b\u9898\u6570\u91cf\u662fLDA\u5efa\u6a21\u4e2d\u7684\u5173\u952e\u6b65\u9aa4\u4e4b\u4e00\u3002\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u6765\u786e\u5b9a\u8fd9\u4e2a\u6570\u91cf\uff0c\u5305\u62ec\u4f7f\u7528\u56f0\u60d1\u5ea6\uff08perplexity\uff09\u548c\u4e3b\u9898\u4e00\u81f4\u6027\uff08topic coherence\uff09\u7b49\u8bc4\u4ef7\u6307\u6807\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u8f83\u4f4e\u7684\u56f0\u60d1\u5ea6\u503c\u548c\u8f83\u9ad8\u7684\u4e3b\u9898\u4e00\u81f4\u6027\u5206\u6570\u610f\u5473\u7740\u66f4\u597d\u7684\u6a21\u578b\u8868\u73b0\u3002\u6b64\u5916\uff0c\u5b9e\u9a8c\u4e0d\u540c\u7684\u4e3b\u9898\u6570\u91cf\uff0c\u5e76\u7ed3\u5408\u9886\u57df\u77e5\u8bc6\u6765\u5224\u65ad\u4e3b\u9898\u7684\u53ef\u89e3\u91ca\u6027\uff0c\u4e5f\u662f\u4e00\u4e2a\u6709\u6548\u7684\u7b56\u7565\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u6784\u5efaLDA\u7684\u65b9\u6cd5\u6709\u591a\u79cd\u3001\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662f\u4f7f\u7528Gensim\u5e93\u3001LDA\u662f\u4e00\u79cd\u4e3b\u9898\u6a21\u578b\uff0c\u7528\u4e8e\u4ece\u6587\u672c\u6570\u636e\u4e2d\u53d1 […]","protected":false},"author":3,"featured_media":932369,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/932366"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=932366"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/932366\/revisions"}],"predecessor-version":[{"id":932370,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/932366\/revisions\/932370"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/932369"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=932366"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=932366"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=932366"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}