{"id":956282,"date":"2024-12-27T02:32:10","date_gmt":"2024-12-26T18:32:10","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/956282.html"},"modified":"2024-12-27T02:32:13","modified_gmt":"2024-12-26T18:32:13","slug":"%e5%a6%82%e4%bd%95%e8%ae%a9python%e8%af%86%e5%88%ab%e5%8d%95%e8%af%8d","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/956282.html","title":{"rendered":"\u5982\u4f55\u8ba9python\u8bc6\u522b\u5355\u8bcd"},"content":{"rendered":"

\"\u5982\u4f55\u8ba9python\u8bc6\u522b\u5355\u8bcd\"<\/p>\n

\u5f00\u5934\u6bb5\u843d:
\u5728Python\u4e2d\u8bc6\u522b\u5355\u8bcd\u7684\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\uff08\u5982NLTK\u548cspaCy\uff09\u3001\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u3002<\/strong> \u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u662f\u4e00\u79cd\u7b80\u5355\u800c\u76f4\u63a5\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u5b9a\u4e49\u7279\u5b9a\u7684\u6a21\u5f0f\u6765\u5339\u914d\u6587\u672c\u4e2d\u7684\u5355\u8bcd\u3002\u7136\u800c\uff0c\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u8bed\u8a00\u5904\u7406\u4efb\u52a1\uff0c\u6bd4\u5982\u8bed\u4e49\u5206\u6790\u6216\u8005\u8bcd\u6027\u6807\u6ce8\uff0c\u4f7f\u7528\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\u5982NLTK\uff08Natural Language Toolkit\uff09\u548cspaCy\u53ef\u80fd\u66f4\u4e3a\u9002\u5408\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u53ef\u4ee5\u5e2e\u52a9\u5f00\u53d1\u8005\u8f7b\u677e\u5b9e\u73b0\u5355\u8bcd\u8bc6\u522b\u548c\u5904\u7406\u3002\u6b64\u5916\uff0c\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u4e5f\u53ef\u7528\u4e8e\u8bad\u7ec3\u6a21\u578b\u6765\u8bc6\u522b\u7279\u5b9a\u5355\u8bcd\u6a21\u5f0f\u6216\u8fdb\u884c\u66f4\u9ad8\u7ea7\u7684\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u53ca\u5176\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u8fdb\u884c\u5355\u8bcd\u8bc6\u522b<\/p>\n<\/p>\n

\u6b63\u5219\u8868\u8fbe\u5f0f\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u6587\u672c\u5904\u7406\u5de5\u5177\uff0c\u80fd\u591f\u5728\u5b57\u7b26\u4e32\u4e2d\u67e5\u627e\u548c\u5339\u914d\u7279\u5b9a\u7684\u6a21\u5f0f\u3002\u5bf9\u4e8e\u8bc6\u522b\u5355\u8bcd\uff0c\u6b63\u5219\u8868\u8fbe\u5f0f\u53ef\u4ee5\u975e\u5e38\u9ad8\u6548\u5730\u5b8c\u6210\u4efb\u52a1\u3002Python\u7684re<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u6765\u5904\u7406\u6b63\u5219\u8868\u8fbe\u5f0f\u3002<\/p>\n<\/p>\n

    \n
  1. \u57fa\u672c\u6982\u5ff5\u4e0e\u4f7f\u7528<\/li>\n<\/ol>\n

    \u6b63\u5219\u8868\u8fbe\u5f0f\u901a\u8fc7\u5b9a\u4e49\u6a21\u5f0f\u6765\u5339\u914d\u6587\u672c\u4e2d\u7684\u7ed3\u6784\u3002\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\u662f\u4f7f\u7528\\b<\/code>\u6765\u5339\u914d\u5355\u8bcd\u8fb9\u754c\uff0c\u4ece\u800c\u8bc6\u522b\u5355\u8bcd\u3002\u4f8b\u5982\uff0c\\b\\w+\\b<\/code>\u53ef\u4ee5\u5339\u914d\u4efb\u610f\u5355\u8bcd\u5b57\u7b26\u5e8f\u5217\u3002Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528re.findall()<\/code>\u51fd\u6570\u6765\u67e5\u627e\u6240\u6709\u5339\u914d\u7684\u5355\u8bcd\u3002<\/p>\n<\/p>\n

    import re<\/p>\n
    text = "This is a sample text with several words."<\/p>\n
    words = re.findall(r'\\b\\w+\\b', text)<\/p>\n
    print(words)  # Output: ['This', 'is', 'a', 'sample', 'text', 'with', 'several', 'words']<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u9ad8\u7ea7\u5339\u914d\u6a21\u5f0f<\/li>\n<\/ol>\n
      \u9664\u4e86\u7b80\u5355\u7684\u5355\u8bcd\u5339\u914d\uff0c\u6b63\u5219\u8868\u8fbe\u5f0f\u8fd8\u53ef\u4ee5\u7528\u4e8e\u8bc6\u522b\u66f4\u590d\u6742\u7684\u8bed\u8a00\u7ed3\u6784\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u4f7f\u7528\u5206\u7ec4\u548c\u9009\u62e9\u7b26\uff0c\u53ef\u4ee5\u5339\u914d\u7279\u5b9a\u7c7b\u578b\u7684\u5355\u8bcd\uff0c\u6216\u8005\u901a\u8fc7\u8d1f\u5411\u67e5\u627e\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u6392\u9664\u903b\u8f91\u3002\u8fd9\u4f7f\u5f97\u6b63\u5219\u8868\u8fbe\u5f0f\u5728\u5904\u7406\u8bed\u8a00\u8bc6\u522b\u4efb\u52a1\u65f6\u975e\u5e38\u7075\u6d3b\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93NLTK<\/p>\n<\/p>\n
      NLTK\uff08Natural Language Toolkit\uff09\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u8d44\u6e90\u6765\u5904\u7406\u6587\u672c\u6570\u636e\uff0c\u5305\u62ec\u8bcd\u6027\u6807\u6ce8\u3001\u6587\u672c\u5206\u7c7b\u3001\u8bed\u6cd5\u89e3\u6790\u7b49\u529f\u80fd\u3002<\/p>\n<\/p>\n
        \n
      1. \u521d\u59cb\u5316\u548c\u57fa\u672c\u4f7f\u7528<\/li>\n<\/ol>\n
        \u9996\u5148\u9700\u8981\u5b89\u88c5NLTK\u5e93\uff0c\u5e76\u4e0b\u8f7d\u5fc5\u8981\u7684\u8bed\u6599\u5e93\u8d44\u6e90\u3002NLTK\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u6765\u5904\u7406\u6587\u672c\u4e2d\u7684\u5355\u8bcd\u8bc6\u522b\u4efb\u52a1\u3002<\/p>\n<\/p>\n
        import nltk<\/p>\n
        nltk.download('punkt')<\/p>\n
        from nltk.tokenize import word_tokenize<\/p>\n
        text = "This is a sample text with several words."<\/p>\n
        words = word_tokenize(text)<\/p>\n
        print(words)  # Output: ['This', 'is', 'a', 'sample', 'text', 'with', 'several', 'words', '.']<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u9ad8\u7ea7\u529f\u80fd\uff1a\u8bcd\u6027\u6807\u6ce8\u548c\u547d\u540d\u5b9e\u4f53\u8bc6\u522b<\/li>\n<\/ol>\n
          NLTK\u4e0d\u4ec5\u53ef\u4ee5\u8bc6\u522b\u5355\u8bcd\uff0c\u8fd8\u53ef\u4ee5\u8fdb\u884c\u8bcd\u6027\u6807\u6ce8\u548c\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u7b49\u66f4\u9ad8\u7ea7\u7684\u4efb\u52a1\u3002\u8fd9\u4e9b\u529f\u80fd\u5bf9\u4e8e\u7406\u89e3\u6587\u672c\u7684\u8bed\u4e49\u7ed3\u6784\u548c\u4e0a\u4e0b\u6587\u4fe1\u606f\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
          nltk.download('averaged_perceptron_tagger')<\/p>\n
          tagged_words = nltk.pos_tag(words)<\/p>\n
          print(tagged_words)  # Output: [('This', 'DT'), ('is', 'VBZ'), ...]<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93spaCy<\/p>\n<\/p>\n
          spaCy\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\uff0c\u4e13\u6ce8\u4e8e\u9ad8\u6548\u7684\u6587\u672c\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u96c6\u6210\u3002\u5b83\u63d0\u4f9b\u4e86\u66f4\u73b0\u4ee3\u5316\u7684API\u548c\u66f4\u9ad8\u6548\u7684\u6027\u80fd\uff0c\u9002\u5408\u5927\u89c4\u6a21\u6587\u672c\u5904\u7406\u4efb\u52a1\u3002<\/p>\n<\/p>\n
            \n
          1. \u5b89\u88c5\u548c\u57fa\u672c\u4f7f\u7528<\/li>\n<\/ol>\n
            spaCy\u63d0\u4f9b\u4e86\u9884\u8bad\u7ec3\u7684\u8bed\u8a00\u6a21\u578b\uff0c\u53ef\u4ee5\u5feb\u901f\u5e94\u7528\u4e8e\u6587\u672c\u5206\u6790\u4efb\u52a1\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5spaCy\u548c\u4e0b\u8f7d\u76f8\u5e94\u7684\u8bed\u8a00\u6a21\u578b\u3002<\/p>\n<\/p>\n
            import spacy<\/p>\n
            nlp = spacy.load("en_core_web_sm")<\/p>\n
            doc = nlp("This is a sample text with several words.")<\/p>\n
            words = [token.text for token in doc]<\/p>\n
            print(words)  # Output: ['This', 'is', 'a', 'sample', 'text', 'with', 'several', 'words', '.']<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u9ad8\u7ea7\u529f\u80fd\uff1a\u4f9d\u5b58\u89e3\u6790\u548c\u5b9e\u4f53\u8bc6\u522b<\/li>\n<\/ol>\n
              spaCy\u4e0d\u4ec5\u53ef\u4ee5\u8bc6\u522b\u5355\u8bcd\uff0c\u8fd8\u80fd\u8fdb\u884c\u4f9d\u5b58\u89e3\u6790\u548c\u5b9e\u4f53\u8bc6\u522b\u7b49\u66f4\u590d\u6742\u7684\u4efb\u52a1\u3002\u8fd9\u4e9b\u529f\u80fd\u53ef\u4ee5\u63d0\u4f9b\u66f4\u6df1\u5c42\u6b21\u7684\u6587\u672c\u7406\u89e3\u80fd\u529b\u3002<\/p>\n<\/p>\n
              for token in doc:<\/p>\n
                  print(token.text, token.pos_, token.dep_)<\/p>\n
              for ent in doc.ents:<\/p>\n
                  print(ent.text, ent.label_)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u8fdb\u884c\u5355\u8bcd\u8bc6\u522b<\/p>\n<\/p>\n
              \u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u53ef\u4ee5\u7528\u4e8e\u8bad\u7ec3\u6a21\u578b\u6765\u8bc6\u522b\u7279\u5b9a\u7684\u5355\u8bcd\u6a21\u5f0f\u6216\u8fdb\u884c\u66f4\u9ad8\u7ea7\u7684\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u3002\u901a\u8fc7\u6784\u5efa\u548c\u8bad\u7ec3\u81ea\u5b9a\u4e49\u6a21\u578b\uff0c\u53ef\u4ee5\u5b9e\u73b0\u66f4\u7075\u6d3b\u548c\u667a\u80fd\u7684\u5355\u8bcd\u8bc6\u522b\u3002<\/p>\n<\/p>\n
                \n
              1. \u57fa\u7840\uff1a\u6570\u636e\u51c6\u5907\u4e0e\u7279\u5f81\u5de5\u7a0b<\/li>\n<\/ol>\n
                \u5728\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u4e4b\u524d\uff0c\u9700\u8981\u51c6\u5907\u597d\u6570\u636e\u96c6\uff0c\u5e76\u8fdb\u884c\u7279\u5f81\u5de5\u7a0b\u4ee5\u63d0\u53d6\u6709\u7528\u7684\u4fe1\u606f\u3002\u901a\u5e38\u9700\u8981\u5bf9\u6587\u672c\u8fdb\u884c\u9884\u5904\u7406\uff0c\u6bd4\u5982\u53bb\u9664\u505c\u7528\u8bcd\u3001\u8bcd\u5e72\u63d0\u53d6\u7b49\u3002<\/p>\n<\/p>\n
                  \n
                1. \u6784\u5efa\u548c\u8bad\u7ec3\u6a21\u578b<\/li>\n<\/ol>\n
                  \u53ef\u4ee5\u4f7f\u7528Scikit-learn\u7b49\u5e93\u6765\u6784\u5efa\u548c\u8bad\u7ec3\u673a\u5668\u5b66\u4e60\u6a21\u578b\u3002\u5e38\u7528\u7684\u7b97\u6cd5\u5305\u62ec\u6734\u7d20\u8d1d\u53f6\u65af\u3001\u652f\u6301\u5411\u91cf\u673a\u548c\u795e\u7ecf\u7f51\u7edc\u7b49\u3002<\/p>\n<\/p>\n
                  from sklearn.feature_extraction.text import CountVectorizer<\/p>\n
                  from sklearn.nAI<\/a>ve_bayes import MultinomialNB<\/p>\n
                  \u6837\u672c\u6570\u636e\u548c\u6807\u7b7e<\/strong><\/h2>\n
                  texts = ["This is a positive text.", "This is a negative text."]<\/p>\n
                  labels = [1, 0]<\/p>\n
                  vectorizer = CountVectorizer()<\/p>\n
                  X = vectorizer.fit_transform(texts)<\/p>\n
                  model = MultinomialNB()<\/p>\n
                  model.fit(X, labels)<\/p>\n
                  \u9884\u6d4b<\/strong><\/h2>\n
                  sample_text = "A positive sample."<\/p>\n
                  sample_X = vectorizer.transform([sample_text])<\/p>\n
                  prediction = model.predict(sample_X)<\/p>\n
                  print(prediction)  # Output: [1]<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u5b9e\u9645\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
                  \u5355\u8bcd\u8bc6\u522b\u5728\u8bb8\u591a\u5b9e\u9645\u5e94\u7528\u573a\u666f\u4e2d\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u5e94\u7528\u9886\u57df\uff1a<\/p>\n<\/p>\n
                    \n
                  1. \u60c5\u611f\u5206\u6790<\/li>\n<\/ol>\n
                    \u901a\u8fc7\u8bc6\u522b\u6587\u672c\u4e2d\u7684\u5173\u952e\u5355\u8bcd\u548c\u77ed\u8bed\uff0c\u53ef\u4ee5\u5206\u6790\u7528\u6237\u8bc4\u8bba\u6216\u793e\u4ea4\u5a92\u4f53\u5e16\u5b50\u4e2d\u7684\u60c5\u611f\u503e\u5411\uff0c\u5e2e\u52a9\u4f01\u4e1a\u4e86\u89e3\u7528\u6237\u53cd\u9988\u548c\u5e02\u573a\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u6587\u672c\u5206\u7c7b<\/li>\n<\/ol>\n
                      \u5728\u65b0\u95fb\u5206\u7c7b\u3001\u5783\u573e\u90ae\u4ef6\u68c0\u6d4b\u7b49\u4efb\u52a1\u4e2d\uff0c\u5355\u8bcd\u8bc6\u522b\u662f\u6587\u672c\u5206\u7c7b\u7684\u57fa\u7840\u6b65\u9aa4\u3002\u901a\u8fc7\u63d0\u53d6\u6587\u672c\u7279\u5f81\uff0c\u8bad\u7ec3\u5206\u7c7b\u6a21\u578b\uff0c\u53ef\u4ee5\u5b9e\u73b0\u81ea\u52a8\u5316\u7684\u6587\u672c\u5206\u7c7b\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u8bed\u4e49\u641c\u7d22<\/li>\n<\/ol>\n
                        \u5728\u641c\u7d22\u5f15\u64ce\u6216\u4fe1\u606f\u68c0\u7d22\u7cfb\u7edf\u4e2d\uff0c\u8bc6\u522b\u548c\u7406\u89e3\u7528\u6237\u67e5\u8be2\u4e2d\u7684\u5173\u952e\u5355\u8bcd\u5bf9\u4e8e\u63d0\u9ad8\u641c\u7d22\u7ed3\u679c\u7684\u76f8\u5173\u6027\u548c\u51c6\u786e\u6027\u81f3\u5173\u91cd\u8981\u3002<\/p>\n<\/p>\n
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