{"id":1021445,"date":"2024-12-27T13:23:05","date_gmt":"2024-12-27T05:23:05","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1021445.html"},"modified":"2024-12-27T13:23:07","modified_gmt":"2024-12-27T05:23:07","slug":"%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8python%e6%a3%80%e6%b5%8b%e6%96%87%e7%8c%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1021445.html","title":{"rendered":"\u5982\u4f55\u4f7f\u7528python\u68c0\u6d4b\u6587\u732e"},"content":{"rendered":"

\"\u5982\u4f55\u4f7f\u7528python\u68c0\u6d4b\u6587\u732e\"<\/p>\n

\u4f7f\u7528Python\u68c0\u6d4b\u6587\u732e\u4e3b\u8981\u6d89\u53ca\u6587\u672c\u5904\u7406\u3001\u76f8\u4f3c\u6027\u8ba1\u7b97\u3001\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5<\/strong>\u3002\u5176\u4e2d\uff0c\u6587\u672c\u5904\u7406\u662f\u57fa\u7840\uff0c\u6d89\u53ca\u6587\u732e\u7684\u89e3\u6790\u4e0e\u6e05\u6d17\uff1b\u76f8\u4f3c\u6027\u8ba1\u7b97\u901a\u8fc7\u7b97\u6cd5\u6765\u5224\u65ad\u4e24\u7bc7\u6587\u732e\u7684\u76f8\u4f3c\u7a0b\u5ea6\uff1b\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5219\u53ef\u7528\u4e8e\u66f4\u9ad8\u7ea7\u7684\u6587\u732e\u5206\u7c7b\u4e0e\u805a\u7c7b\u3002\u4e00\u4e2a\u8be6\u7ec6\u7684\u6b65\u9aa4\u53ef\u80fd\u5305\u62ec\uff1a\u9996\u5148\uff0c\u4f7f\u7528\u6587\u672c\u5904\u7406\u5de5\u5177\u5982NLTK\u6216spaCy\u8fdb\u884c\u6587\u732e\u9884\u5904\u7406<\/strong>\uff0c\u8fd9\u5305\u62ec\u53bb\u9664\u505c\u7528\u8bcd\u3001\u8bcd\u5f62\u8fd8\u539f\u7b49\uff1b\u7136\u540e\uff0c\u5229\u7528TF-IDF\u6216Word2Vec\u7b49\u6280\u672f\u8fdb\u884c\u6587\u732e\u5411\u91cf\u5316<\/strong>\uff0c\u4ee5\u4fbf\u4e8e\u8ba1\u7b97\u6587\u732e\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\uff1b\u6700\u540e\uff0c\u5e94\u7528\u76f8\u4f3c\u6027\u7b97\u6cd5\uff08\u5982\u4f59\u5f26\u76f8\u4f3c\u6027\uff09\u6216\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff08\u5982SVM\u3001K-Means\uff09\u8fdb\u884c\u76f8\u4f3c\u6027\u68c0\u6d4b\u6216\u5206\u7c7b<\/strong>\u3002\u5728\u4ee5\u4e0b\u5185\u5bb9\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u6bcf\u4e00\u4e2a\u6b65\u9aa4\u548c\u6280\u672f\uff0c\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u901a\u8fc7Python\u5b9e\u73b0\u6587\u732e\u68c0\u6d4b\u3002<\/p>\n<\/p>\n

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

\u6587\u672c\u9884\u5904\u7406\u662f\u6587\u732e\u68c0\u6d4b\u7684\u57fa\u7840\u6b65\u9aa4\u3002\u5b83\u5305\u62ec\u6587\u6863\u7684\u89e3\u6790\u3001\u6e05\u6d17\u548c\u89c4\u8303\u5316\u3002\u5bf9\u4e8ePDF\u683c\u5f0f\u7684\u6587\u732e\uff0c\u53ef\u4ee5\u4f7f\u7528PDF\u89e3\u6790\u5e93\u5982PyPDF2\u3001pdfminer.six\u8fdb\u884c\u89e3\u6790\uff0c\u5c06\u6587\u732e\u5185\u5bb9\u63d0\u53d6\u4e3a\u6587\u672c\u683c\u5f0f\u3002\u5bf9\u63d0\u53d6\u7684\u6587\u672c\u8fdb\u884c\u6e05\u6d17\uff0c\u53bb\u9664\u4e0d\u5fc5\u8981\u7684\u5b57\u7b26\u548c\u683c\u5f0f\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528NLTK\u6216spaCy\u8fdb\u884c\u5206\u8bcd\u3001\u53bb\u9664\u505c\u7528\u8bcd\u548c\u8bcd\u5f62\u8fd8\u539f\u3002\u8fd9\u4e9b\u64cd\u4f5c\u6709\u52a9\u4e8e\u51cf\u5c11\u6587\u672c\u566a\u97f3\uff0c\u63d0\u9ad8\u540e\u7eed\u76f8\u4f3c\u6027\u8ba1\u7b97\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n

\u5206\u8bcd\u662f\u5c06\u6587\u732e\u5185\u5bb9\u5206\u6210\u4e00\u4e2a\u4e2a\u5355\u8bcd\u7684\u8fc7\u7a0b\uff0c\u662f\u540e\u7eed\u6587\u672c\u5206\u6790\u7684\u57fa\u7840\u3002\u53bb\u9664\u505c\u7528\u8bcd\u662f\u4e3a\u4e86\u51cf\u5c11\u6587\u672c\u4e2d\u7684\u65e0\u610f\u4e49\u8bcd\u6c47\uff0c\u5982\u201c\u7684\u201d\u3001\u201c\u662f\u201d\u7b49\uff0c\u63d0\u9ad8\u6709\u6548\u4fe1\u606f\u7684\u6bd4\u4f8b\u3002\u8bcd\u5f62\u8fd8\u539f\u5219\u662f\u5c06\u5355\u8bcd\u8fd8\u539f\u4e3a\u8bcd\u6839\u5f62\u5f0f\uff0c\u4f8b\u5982\u5c06\u201crunning\u201d\u8fd8\u539f\u4e3a\u201crun\u201d\uff0c\u4ece\u800c\u51cf\u5c11\u8bcd\u6c47\u7684\u591a\u6837\u6027\uff0c\u63d0\u9ad8\u76f8\u4f3c\u6027\u8ba1\u7b97\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u6587\u732e\u5411\u91cf\u5316<\/p>\n<\/p>\n

\u5728\u5b8c\u6210\u6587\u672c\u9884\u5904\u7406\u540e\uff0c\u4e0b\u4e00\u6b65\u662f\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u53ef\u4ee5\u8fdb\u884c\u8ba1\u7b97\u7684\u5411\u91cf\u5f62\u5f0f\u3002TF-IDF\uff08Term Frequency-Inverse Document Frequency\uff09\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6587\u672c\u5411\u91cf\u5316\u65b9\u6cd5\u3002\u5b83\u901a\u8fc7\u8ba1\u7b97\u5355\u8bcd\u5728\u6587\u6863\u4e2d\u7684\u51fa\u73b0\u9891\u7387\u548c\u5728\u6574\u4e2a\u6587\u6863\u96c6\u4e2d\u7684\u9006\u6587\u6863\u9891\u7387\uff0c\u6765\u8861\u91cf\u4e00\u4e2a\u5355\u8bcd\u5bf9\u7279\u5b9a\u6587\u6863\u7684\u91cd\u8981\u6027\u3002\u8fd9\u79cd\u65b9\u6cd5\u80fd\u591f\u6709\u6548\u5730\u8868\u793a\u6587\u672c\u7684\u7279\u5f81\u3002<\/p>\n<\/p>\n

\u9664\u4e86TF-IDF\uff0cWord2Vec\u4e5f\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6587\u672c\u5411\u91cf\u5316\u6280\u672f\u3002\u5b83\u901a\u8fc7\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u5b66\u4e60\u5355\u8bcd\u7684\u4e0a\u4e0b\u6587\u5173\u7cfb\uff0c\u5c06\u5355\u8bcd\u6620\u5c04\u4e3a\u5411\u91cf\u7a7a\u95f4\u4e2d\u7684\u4e00\u4e2a\u70b9\uff0c\u4ece\u800c\u6355\u6349\u5355\u8bcd\u4e4b\u95f4\u7684\u8bed\u4e49\u5173\u7cfb\u3002Word2Vec\u751f\u6210\u7684\u5411\u91cf\u4e0d\u4ec5\u80fd\u7528\u4e8e\u76f8\u4f3c\u6027\u8ba1\u7b97\uff0c\u8fd8\u80fd\u7528\u4e8e\u66f4\u9ad8\u7ea7\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u3002<\/p>\n<\/p>\n

\u4e09\u3001\u76f8\u4f3c\u6027\u8ba1\u7b97<\/p>\n<\/p>\n

\u4e00\u65e6\u6587\u732e\u88ab\u5411\u91cf\u5316\uff0c\u5c31\u53ef\u4ee5\u8fdb\u884c\u76f8\u4f3c\u6027\u8ba1\u7b97\u3002\u4f59\u5f26\u76f8\u4f3c\u6027\u662f\u6700\u5e38\u7528\u7684\u76f8\u4f3c\u6027\u5ea6\u91cf\u65b9\u6cd5\u4e4b\u4e00\u3002\u5b83\u8ba1\u7b97\u4e24\u4e2a\u5411\u91cf\u4e4b\u95f4\u7684\u4f59\u5f26\u5939\u89d2\uff0c\u4ee5\u6b64\u5ea6\u91cf\u5411\u91cf\u7684\u76f8\u4f3c\u5ea6\u3002\u4f59\u5f26\u76f8\u4f3c\u6027\u503c\u4ecb\u4e8e0\u52301\u4e4b\u95f4\uff0c\u503c\u8d8a\u63a5\u8fd11\u8868\u793a\u4e24\u4e2a\u5411\u91cf\u8d8a\u76f8\u4f3c\u3002<\/p>\n<\/p>\n

Jaccard\u76f8\u4f3c\u6027\u662f\u53e6\u4e00\u79cd\u5e38\u7528\u7684\u76f8\u4f3c\u6027\u5ea6\u91cf\u65b9\u6cd5\uff0c\u4e3b\u8981\u7528\u4e8e\u96c6\u5408\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u8ba1\u7b97\u3002\u5b83\u901a\u8fc7\u8ba1\u7b97\u4e24\u4e2a\u96c6\u5408\u7684\u4ea4\u96c6\u4e0e\u5e76\u96c6\u7684\u6bd4\u503c\u6765\u8861\u91cf\u76f8\u4f3c\u5ea6\u3002\u5728\u6587\u732e\u68c0\u6d4b\u4e2d\uff0c\u53ef\u4ee5\u5c06\u6587\u6863\u8868\u793a\u4e3a\u5355\u8bcd\u96c6\u5408\uff0c\u4f7f\u7528Jaccard\u76f8\u4f3c\u6027\u6765\u5ea6\u91cf\u6587\u6863\u7684\u76f8\u4f3c\u6027\u3002<\/p>\n<\/p>\n

\u56db\u3001\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5e94\u7528<\/p>\n<\/p>\n

\u5728\u76f8\u4f3c\u6027\u8ba1\u7b97\u7684\u57fa\u7840\u4e0a\uff0c\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u53ef\u4ee5\u7528\u4e8e\u66f4\u9ad8\u7ea7\u7684\u6587\u732e\u68c0\u6d4b\u4efb\u52a1\uff0c\u5982\u6587\u732e\u5206\u7c7b\u548c\u805a\u7c7b\u3002\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u662f\u4e00\u79cd\u5e38\u7528\u7684\u5206\u7c7b\u7b97\u6cd5\uff0c\u5b83\u901a\u8fc7\u5bfb\u627e\u6700\u5927\u5316\u7c7b\u95f4\u8ddd\u79bb\u7684\u8d85\u5e73\u9762\uff0c\u5b9e\u73b0\u6587\u732e\u7684\u81ea\u52a8\u5206\u7c7b\u3002\u53ef\u4ee5\u5c06\u6587\u732e\u7684\u5411\u91cf\u8868\u793a\u4f5c\u4e3aSVM\u7684\u8f93\u5165\u7279\u5f81\uff0c\u5b9e\u73b0\u4e0d\u540c\u7c7b\u522b\u6587\u732e\u7684\u81ea\u52a8\u5206\u7c7b\u3002<\/p>\n<\/p>\n

K-Means\u805a\u7c7b\u7b97\u6cd5\u53ef\u4ee5\u7528\u4e8e\u6587\u732e\u7684\u805a\u7c7b\u5206\u6790\u3002\u901a\u8fc7\u5c06\u6587\u732e\u5411\u91cf\u5316\u540e\u7684\u6570\u636e\u8f93\u5165K-Means\u7b97\u6cd5\uff0c\u80fd\u591f\u81ea\u52a8\u8bc6\u522b\u6587\u732e\u7684\u6f5c\u5728\u4e3b\u9898\u6216\u805a\u7c7b\u7279\u5f81\uff0c\u5b9e\u73b0\u6587\u732e\u7684\u805a\u7c7b\u5206\u6790\u3002\u8fd9\u5bf9\u4e8e\u5927\u89c4\u6a21\u6587\u732e\u7684\u7ec4\u7ec7\u548c\u7ba1\u7406\u5177\u6709\u91cd\u8981\u610f\u4e49\u3002<\/p>\n<\/p>\n

\u4e94\u3001Python\u5b9e\u73b0\u793a\u4f8b<\/p>\n<\/p>\n

\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7f16\u5199\u4e00\u4e2a\u5b8c\u6574\u7684\u6587\u732e\u68c0\u6d4b\u7cfb\u7edf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684Python\u5b9e\u73b0\u793a\u4f8b\uff0c\u6f14\u793a\u5982\u4f55\u4f7f\u7528\u4e0a\u8ff0\u6280\u672f\u5b9e\u73b0\u6587\u732e\u7684\u76f8\u4f3c\u6027\u68c0\u6d4b\uff1a<\/p>\n<\/p>\n

import nltk<\/p>\n
from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
from sklearn.metrics.pAI<\/a>rwise import cosine_similarity<\/p>\n
\u6587\u732e\u6587\u672c<\/strong><\/h2>\n
doc1 = "This is a sample document."<\/p>\n
doc2 = "This document is a sample."<\/p>\n
\u6587\u672c\u9884\u5904\u7406<\/strong><\/h2>\n
nltk.download('punkt')<\/p>\n
tokens1 = nltk.word_tokenize(doc1)<\/p>\n
tokens2 = nltk.word_tokenize(doc2)<\/p>\n
TF-IDF\u5411\u91cf\u5316<\/strong><\/h2>\n
vectorizer = TfidfVectorizer()<\/p>\n
tfidf_matrix = vectorizer.fit_transform([' '.join(tokens1), ' '.join(tokens2)])<\/p>\n
\u8ba1\u7b97\u4f59\u5f26\u76f8\u4f3c\u6027<\/strong><\/h2>\n
cos_sim = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])<\/p>\n
print(f"Document similarity: {cos_sim[0][0]}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4ee5\u4e0a\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u6587\u732e\u7684\u76f8\u4f3c\u6027\u68c0\u6d4b\u3002\u901a\u8fc7\u6587\u672c\u9884\u5904\u7406\u3001TF-IDF\u5411\u91cf\u5316\u548c\u4f59\u5f26\u76f8\u4f3c\u6027\u8ba1\u7b97\uff0c\u80fd\u591f\u5b9e\u73b0\u5bf9\u4e24\u7bc7\u6587\u732e\u76f8\u4f3c\u5ea6\u7684\u81ea\u52a8\u68c0\u6d4b\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u6269\u5c55\u548c\u4f18\u5316\u8be5\u4ee3\u7801\uff0c\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u6587\u732e\u68c0\u6d4b\u573a\u666f\u3002<\/p>\n<\/p>\n
\u516d\u3001\u6587\u732e\u68c0\u6d4b\u7684\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
\u6587\u732e\u68c0\u6d4b\u6280\u672f\u5728\u591a\u4e2a\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4f8b\u5982\uff0c\u5728\u5b66\u672f\u7814\u7a76\u4e2d\uff0c\u6587\u732e\u68c0\u6d4b\u53ef\u4ee5\u7528\u4e8e\u67e5\u91cd\uff0c\u68c0\u6d4b\u6587\u732e\u4e4b\u95f4\u7684\u91cd\u590d\u7387\uff0c\u9632\u6b62\u5b66\u672f\u4e0d\u7aef\u884c\u4e3a\u3002\u5728\u4fe1\u606f\u68c0\u7d22\u4e2d\uff0c\u6587\u732e\u68c0\u6d4b\u6280\u672f\u53ef\u4ee5\u63d0\u9ad8\u68c0\u7d22\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u76f8\u5173\u6027\uff0c\u5e2e\u52a9\u7528\u6237\u5feb\u901f\u627e\u5230\u6240\u9700\u4fe1\u606f\u3002<\/p>\n<\/p>\n
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