{"id":2498,"date":"2021-04-11T21:07:46","date_gmt":"2021-04-11T13:07:46","guid":{"rendered":"https:\/\/achieve.dhcn.cn\/?p=2498"},"modified":"2024-04-04T01:23:19","modified_gmt":"2024-04-03T17:23:19","slug":"%e5%9f%ba%e4%ba%8etopwords%e6%96%b9%e6%b3%95%e7%9a%84%e5%8f%a4%e6%96%87%e7%8c%ae%e4%b8%93%e5%90%8d%e8%af%86%e5%88%ab-%e4%bb%a5%e3%80%8a%e6%b1%89%e4%b9%a6%e3%80%8b%e5%92%8c%e3%80%8a","status":"publish","type":"post","link":"https:\/\/achieve.dhcn.cn\/en\/site\/works\/dhjournal\/202002\/2498.html","title":{"rendered":"\u57fa\u4e8eTopWORDS\u65b9\u6cd5\u7684\u53e4\u6587\u732e\u4e13\u540d\u8bc6\u522b\u2014\u2014\u4ee5\u300a\u6c49\u4e66\u300b\u548c\u300a\u660e\u53f2\u300b\u4e3a\u4f8b"},"content":{"rendered":"

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Infrastructure<\/span><\/strong><\/p>\n\n\n\n

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———————————————-<\/span><\/p>\n\n\n\n

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———————————————-<\/span><\/p>\n\n\n\n

\u5f15\u8a00<\/span><\/strong><\/p>\n\n\n\n

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<\/p>\n\n\n\n

(\u4e00)\u6709\u76d1\u7763\u65b9\u6cd5<\/span><\/p>\n\n\n\n

\u6709\u76d1\u7763\u7684\u65b9\u6cd5\u9700\u8981\u9ad8\u8d28\u91cf\u4e14\u5927\u91cf\u7684\u8bad\u7ec3\u8bed\u6599\u548c\u9884\u5148\u7ed9\u5b9a\u7684\u8bcd\u5e93\uff0c\u5728\u7edf\u8ba1\u5b66\u4e60\u548c\u673a\u5668\u5b66\u4e60\u7684\u6a21\u578b\u6846\u67b6\u4e0b\u8fdb\u884c\u5b66\u4e60\uff0c\u5728\u76ee\u6807\u6587\u672c\u4e0e\u8bad\u7ec3\u8bed\u6599\u9ad8\u5ea6\u76f8\u4f3c\u7684\u5c01\u95ed\u6d4b\u8bd5\u4e2d\u80fd\u8fbe\u5230\u975e\u5e38\u7cbe\u51c6\u7684\u8bcd\u8bed\u53d1\u73b0\u548c\u5206\u8bcd\u6548\u679c\u3002\u8bad\u7ec3\u8bed\u6599\u5e93\u4e2d\u7684\u6587\u672c\u9700\u8981\u4eba\u5de5\u8fdb\u884c\u5207\u5206\u548c\u5206\u8bcd\u6807\u6ce8\uff0c\u8017\u65f6\u8017\u529b\u3002\u5728\u9762\u5bf9\u6709\u591a\u79cd\u53ef\u80fd\u7684\u5207\u5206\u9884\u6d4b\u7ed3\u679c\u65f6\uff0c\u6a21\u578b\u901a\u8fc7\u5bf9\u6240\u6709\u7684\u60c5\u51b5\u8fdb\u884c\u7edf\u8ba1\u63a8\u65ad\uff0c\u5728\u4e00\u5b9a\u7684\u51c6\u5219\u4e0b\u9009\u62e9\u6700\u4f18\u7684\u9884\u6d4b\u7ed3\u679c\u3002\u5f53\u76ee\u6807\u6587\u672c\u7684\u7ed3\u6784\u3001\u98ce\u683c\u3001\u7528\u8bcd\u9891\u7387\u4e0e\u8bad\u7ec3\u6587\u672c\u76f8\u540c\u6216\u63a5\u8fd1\u65f6\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u901a\u5e38\u8868\u73b0\u5f97\u5f88\u597d\u3002\u4f46\u662f\uff0c\u5f53\u76ee\u6807\u6587\u672c\u4e2d\u7684\u5b9e\u9645\u7528\u8bcd\u542b\u6709\u672a\u5305\u542b\u4e8e\u9884\u5148\u7ed9\u5b9a\u8bcd\u8868\u4e2d\u7684\u8bcd\u6c47\u65f6\uff0c\u7279\u522b\u662f\u5f53\u76ee\u6807\u6587\u672c\u7684\u5199\u4f5c\u98ce\u683c\u548c\u4e3b\u9898\u4e0e\u8bad\u7ec3\u8bed\u6599\u5e93\u6709\u5f88\u5927\u4e0d\u540c\u65f6\uff0c\u6709\u76d1\u7763\u7684\u65b9\u6cd5\u5f80\u5f80\u65e0\u6cd5\u8bc6\u522b\u5176\u4e2d\u8bb8\u591a\u672a\u767b\u5f55\u7684\u8bcd\u6c47[10]<\/sup>\u3002<\/p>\n\n\n\n

\u6211\u4eec\u4f7f\u7528\u76ee\u524d\u6d41\u884c\u7684\u4e94\u79cd\u6709\u76d1\u7763\u5206\u8bcd\u65b9\u6cd5\u6765\u8fdb\u884c\u6d4b\u8bd5\u3002\u5b83\u4eec\u662fTHU Lexical Analyzer for Chinese (THULAC)[11]<\/sup>\u3001PKUSEG[12]<\/sup>\u3001\u8bed\u8a00\u6280\u672f\u5e73\u53f0(Language Technology Platform, LTP)[13]<\/sup>\u3001Stanford NLP[14]<\/sup>\u548c\u201c\u7ed3\u5df4\u201d\u4e2d\u6587\u5206\u8bcd(JIEBA)[15]<\/sup>\u3002\u5b83\u4eec\u90fd\u662f\u5f00\u6e90\u514d\u8d39\u7684\u8f6f\u4ef6\uff0c\u800c\u4e14\u6613\u4e8e\u4e0b\u8f7d\u548c\u4f7f\u7528\u3002<\/p>\n\n\n\n

THU Lexical Analyzer for Chinese (THULAC)\u662f2016\u5e74\u7531\u6e05\u534e\u5927\u5b66\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u793e\u4f1a\u4eba\u6587\u8ba1\u7b97\u5b9e\u9a8c\u5ba4\u7814\u53d1\u51fa\u7684\u4e2d\u6587\u8bcd\u6cd5\u5206\u6790\u5de5\u5177\u5305\uff0c\u5305\u542b\u5206\u8bcd\u548c\u8bcd\u6027\u6807\u6ce8\u529f\u80fd\u3002\u5b83\u901a\u8fc75,800\u4e07\u5b57\u7684\u4e2d\u6587\u6807\u6ce8\u8bed\u6599\u5e93\u8bad\u7ec3\u800c\u6210\uff0c\u5177\u6709\u80fd\u529b\u5f3a\u3001\u51c6\u786e\u7387\u9ad8\u548c\u901f\u5ea6\u8f83\u5feb\u7684\u4f18\u52bf\u3002<\/p>\n\n\n\n

PKUSEG\u662f2019\u5e74\u7531\u5317\u4eac\u5927\u5b66\u8bed\u8a00\u8ba1\u7b97\u4e0e\u673a\u5668\u5b66\u4e60\u7814\u7a76\u7ec4\u5f00\u53d1\u7684\u4e2d\u6587\u5206\u8bcd\u5de5\u5177\u3002\u8be5\u7b97\u6cd5\u57fa\u4e8e\u6761\u4ef6\u968f\u673a\u573a\u6a21\u578b\uff0c\u53ef\u4ee5\u8fdb\u884c\u4e0d\u540c\u9886\u57df\u7684\u4e2d\u6587\u5206\u8bcd\u3002\u5728\u4f7f\u7528\u76f8\u540c\u8bad\u7ec3\u6570\u636e\u548c\u6d4b\u8bd5\u6570\u636e\u7684\u6761\u4ef6\u4e0b\uff0cPKUSEG\u6709\u7740\u6bd4\u5176\u4ed6\u65b9\u6cd5\u66f4\u9ad8\u7684\u5206\u8bcd\u51c6\u786e\u7387\u3002\u5b83\u8fd8\u652f\u6301\u7528\u6237\u81ea\u8bad\u7ec3\u6a21\u578b\u548c\u8bcd\u6027\u6807\u6ce8\u3002<\/p>\n\n\n\n

\u8bed\u8a00\u6280\u672f\u5e73\u53f0(Language Technology Platform, LTP)\u662f\u7531\u54c8\u5c14\u6ee8\u5de5\u4e1a\u5927\u5b66\u793e\u4f1a\u8ba1\u7b97\u4e0e\u4fe1\u606f\u68c0\u7d22\u7814\u7a76\u4e2d\u5fc3\u5386\u65f6\u5341\u5e74\u5f00\u53d1\u7684\u4e2d\u6587\u8bed\u8a00\u5904\u7406\u5de5\u5177\uff0c\u57282011\u5e74\u5f00\u6e90\u3002\u5b83\u6db5\u76d6\u4e86\u8bcd\u6cd5\u3001\u53e5\u6cd5\u3001\u8bed\u4e49\u7b49\u4e2d\u6587\u5904\u7406\u529f\u80fd\u3002LTP\u5206\u8bcd\u6a21\u5757\u901a\u8fc7\u6700\u5927\u6b63\u5411\u5339\u914d\u65b9\u6cd5\u5f97\u5230\u8bcd\u7279\u5f81\uff0c\u5e76\u5c06\u5176\u878d\u5408\u5230\u8bcd\u5178\u4e2d\u6765\u8fdb\u884c\u7edf\u8ba1\u63a8\u65ad\u548c\u5206\u8bcd\u4efb\u52a1\u3002<\/p>\n\n\n\n

Stanford NLP\u662f\u7531\u65af\u5766\u798f\u81ea\u7136\u8bed\u8a00\u5904\u7406\u56e2\u961f\u5f00\u53d1\u7684\u5206\u8bcd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5904\u7406\u591a\u79cd\u8bed\u8a00\u7684\u5206\u8bcd\u4efb\u52a1\u3002\u8be5\u65b9\u6cd5\u53ef\u4ee5\u627e\u5230\u6c49\u8bed\u53e5\u5b50\u7684\u8bed\u6cd5\u7ed3\u6784\uff0c\u5e76\u501f\u6b64\u8fdb\u884c\u5206\u8bcd\u3002<\/p>\n\n\n\n

\u201c\u7ed3\u5df4\u201d\u4e2d\u6587\u5206\u8bcd(JIEBA)\u662f\u975e\u5e38\u4f18\u79c0\u7684\u5f00\u6e90\u5206\u8bcd\u7b97\u6cd5\uff0c\u6709\u5341\u591a\u79cd\u4e0d\u540c\u7684\u7f16\u7a0b\u8bed\u8a00\u7248\u672c\u3002\u8be5\u7b97\u6cd5\u8fd0\u7528\u52a8\u6001\u89c4\u5212\u65b9\u6cd5\u627e\u5230\u6700\u5927\u6982\u7387\u7684\u5207\u5206\u7ec4\u5408\u8def\u5f84\uff0c\u5e76\u7528\u9690\u9a6c\u5c14\u53ef\u592b\u94fe\u6a21\u578b\u5904\u7406\u672a\u767b\u5f55\u8bcd\u3002\u8be5\u65b9\u6cd5\u662f\u901a\u8fc7\u5927\u91cf\u7684\u4eba\u6c11\u65e5\u62a5\u8bed\u6599\u5e93\u8bad\u7ec3\u5f97\u5230\u7684\u3002<\/p>\n\n\n\n

<\/p>\n\n\n\n

(\u4e8c)\u65e0\u76d1\u7763\u65b9\u6cd5<\/span><\/p>\n\n\n\n

\u65e0\u76d1\u7763\u65b9\u6cd5\u4e0d\u4f9d\u8d56\u4e8e\u5df2\u6807\u6ce8\u7684\u8bcd\u8bed\u5217\u8868\uff0c\u4e5f\u4e0d\u4f9d\u8d56\u4e8e\u7531\u5df2\u5207\u5206\u7684\u8bad\u7ec3\u6587\u672c\u6784\u6210\u7684\u8bad\u7ec3\u8bed\u6599\u5e93\u3002\u4f20\u7edf\u7684\u65e0\u76d1\u7763\u65b9\u6cd5\u4e3b\u8981\u57fa\u4e8eN\u5143\u7ec4(N-gram)\u3001\u8bcd\u8bed\u9891\u7387\u3001\u4e92\u4fe1\u606f\u51c6\u5219\u548c\u5047\u8bbe\u68c0\u9a8c\u7b49[16]<\/sup>\u3002\u57fa\u4e8eN\u5143\u7ec4\u548c\u8bcd\u9891\u7684\u65b9\u6cd5\u5bf9\u6587\u672c\u4e2d\u7684\u9ad8\u9891\u8bcd\u8bed\u8fdb\u884c\u63d0\u53d6\uff0c\u4f46\u662f\u5ffd\u7565\u4e86\u6784\u6210\u8bcd\u8bed\u7684\u5b57\u7b26\u4e4b\u95f4\u7ed3\u5408\u5173\u7cfb\u7684\u663e\u8457\u6027\u3002\u4e92\u4fe1\u606f\u51c6\u5219\u5bf9\u5176\u505a\u4e86\u4e00\u5b9a\u7684\u6539\u5584\uff0c\u8ba1\u7b97\u8054\u5408\u5206\u5e03\u8bcd\u9891\u7684\u540c\u65f6\uff0c\u8fd8\u5229\u7528\u8fb9\u7f18\u5206\u5e03\u8fdb\u884c\u4e86\u5f52\u4e00\u5316\u548c\u6807\u51c6\u5316\uff0c\u6709\u6548\u5730\u523b\u753b\u4e86\u5b57\u7b26\u7ed3\u5408\u7684\u7d27\u5bc6\u7a0b\u5ea6\u3002\u57fa\u4e8e\u5047\u8bbe\u68c0\u9a8c\u7684\u65b9\u6cd5\uff0c\u5982t\u68c0\u9a8c\u3001\u5361\u65b9\u68c0\u9a8c\u3001\u4f3c\u7136\u6bd4\u68c0\u9a8c\u7b49\uff0c\u8bbe\u8ba1\u51fa\u53ef\u4ee5\u8861\u91cf\u5b57\u7b26\u7ed3\u5408\u7a0b\u5ea6\u7684\u6307\u6807\uff0c\u6839\u636e\u68c0\u9a8c\u7edf\u8ba1\u91cf\u7684\u6570\u503c\u5927\u5c0f\u6765\u5224\u65ad\u5207\u5206\u4e0e\u5426\u3002\u57fa\u4e8e\u4e92\u4fe1\u606f\u51c6\u5219\u548c\u5047\u8bbe\u68c0\u9a8c\u7684\u65b9\u6cd5\u90fd\u662f\u5bf9\u6587\u672c\u7684\u5c40\u90e8\u8fdb\u884c\u5efa\u6a21\uff0c\u7f3a\u5c11\u5168\u5c40\u7684\u7edf\u8ba1\u6a21\u578b\uff0c\u8fd8\u9700\u8981\u5bf9\u9608\u503c\u7684\u9009\u53d6\u8fdb\u884c\u989d\u5916\u7684\u9009\u62e9\u3002\u53e6\u5916\uff0c\u5b83\u4eec\u96be\u4ee5\u63d0\u53d6\u6587\u672c\u4e2d\u7684\u591a\u97f3\u8282\u8bcd\u8bed\u548c\u77ed\u8bed\u3002<\/p>\n\n\n\n

TopWORDS\u65b9\u6cd5[17]<\/sup>\u4e8e2016\u5e74\u53d1\u8868\u5728\u300a\u7f8e\u56fd\u79d1\u5b66\u9662\u9662\u520a\u300b(PNAS)\uff0c\u662f\u8fd1\u5e74\u6765\u65e0\u76d1\u7763\u8bcd\u8bed\u53d1\u73b0\u548c\u4e2d\u6587\u5206\u8bcd\u7814\u7a76\u7684\u91cd\u8981\u8fdb\u5c55\u4e4b\u4e00\u3002\u8be5\u65b9\u6cd5\u53ef\u4ee5\u5728\u4e0d\u9700\u8981\u4efb\u4f55\u8bad\u7ec3\u8bed\u6599\u7684\u60c5\u51b5\u4e0b\u8fdb\u884c\u8bcd\u8bed\u53d1\u73b0\u3001\u4e2d\u6587\u5206\u8bcd\uff0c\u5e76\u5bf9\u5b66\u4e60\u5230\u7684\u8bcd\u8bed\u6309\u7167\u53ef\u4fe1\u5ea6\u8fdb\u884c\u6392\u5e8f\u3002\u5b83\u4ece\u76ee\u6807\u6587\u672c\u81ea\u8eab\u4e2d\u8fdb\u884c\u4fe1\u606f\u63d0\u53d6\u548c\u5b66\u4e60\uff0c\u5e76\u5229\u7528\u7edf\u8ba1\u63a8\u65ad\u548c\u6a21\u578b\u9009\u62e9\u6280\u672f\uff0c\u5728\u5904\u7406\u5177\u6709\u5f88\u5f3a\u4e13\u4e1a\u6027\u7684\u4e2d\u6587\u6587\u672c\u65f6\u6709\u7740\u975e\u5e38\u597d\u7684\u6548\u679c\u3002\u5982\u679c\u7814\u7a76\u8005\u63d0\u4f9b\u9884\u5148\u6807\u5b9a\u7684\u8bcd\u8bed\u5217\u8868\u6216\u8bad\u7ec3\u8bed\u6599\u7b49\u5148\u9a8c\u77e5\u8bc6\uff0cTopWORDS\u65b9\u6cd5\u4e5f\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u628a\u8fd9\u4e9b\u5148\u9a8c\u77e5\u8bc6\u52a0\u8f7d\u5230\u7b97\u6cd5\u4e2d\uff0c\u4ee5\u8fdb\u4e00\u6b65\u63d0\u5347\u65b9\u6cd5\u7684\u7a33\u5b9a\u6027\u548c\u51c6\u786e\u7387\u3002\u548c\u5176\u4ed6\u7684\u65e0\u76d1\u7763\u65b9\u6cd5\u76f8\u6bd4\uff0cTopWORDS\u65b9\u6cd5\u6240\u8fd0\u7528\u7edf\u8ba1\u6a21\u578b\u548c\u5206\u6790\u6280\u672f\u4ece\u5168\u5c40\u800c\u975e\u5c40\u90e8\u7684\u89c6\u89d2\u5bf9\u4e2d\u6587\u6587\u672c\u8fdb\u884c\u5efa\u6a21\u4e0e\u5206\u6790\uff0c\u65b0\u8bcd\u53d1\u73b0\u7684\u80fd\u529b\u548c\u7cbe\u51c6\u5ea6\u6709\u5927\u5e45\u5ea6\u7684\u63d0\u9ad8\u3002<\/p>\n\n\n\n

<\/p>\n\n\n\n

1.WDM\u6a21\u578b\u4ecb\u7ecd<\/span><\/p>\n\n\n\n

TopWORDS\u57fa\u4e8e\u8bcd\u5178\u6a21\u578b(word dictionary model, WDM)\u3002\u8bbe\u6587\u672c\u4e2d\u7684\u8bcd\u8bed\u7ec4\u6210\u7684\u8bcd\u5178\u4e3a<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5176\u5bf9\u5e94\u7684\u8bcd\u9891\u4e3a<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

WDM\u8003\u8651\u4e00\u4e2a\u751f\u6210\u6a21\u578b\uff0c\u901a\u8fc7\u6709\u653e\u56de\u968f\u673a\u62bd\u6837\u7684\u65b9\u5f0f\uff0c\u5bf9\u5207\u5206\u7684\u53e5\u5b50\u8fdb\u884c\u5efa\u6a21\u3002\u5bf9\u4e8e\u4e00\u4e2a\u5305\u542bM<\/em>\u4e2a\u8bcd\u8bed\u7684\u53e5\u5b50<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

WDM\u7ed9\u51fa\u7684S<\/em>\u7684\u751f\u6210\u6982\u7387\u4e3a<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6211\u4eec\u8003\u8651\u4e00\u4e2a\u672a\u5207\u5206\u7684\u53e5\u5b50T<\/em>\uff0c\u8bbeS<\/em>(T<\/em>|D<\/em>)\u4e3a(\u5728\u8bcd\u5178D<\/em>\u7684\u6761\u4ef6\u4e0b)\u53e5\u5b50T<\/em>\u7684\u6240\u6709\u53ef\u80fd\u7684\u5207\u5206\u6240\u6784\u6210\u7684\u96c6\u5408\u3002\u90a3\u4e48\uff0cWDM\u7ed9\u51fa\u7684T<\/em>\u7684\u751f\u6210\u6982\u7387\u4e3a<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u4e8e\u662f\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u6587\u672c\u8fdb\u884c\u5207\u5206\uff0c\u901a\u8fc7\u8ba1\u7b97\u6761\u4ef6\u6982\u7387<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u88681  \u8bcd\u5178D<\/em>\u548c\u8bcd\u9891\u03b8<\/em>\u7684\u793a\u4f8b<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

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P(S<\/em>1|T;D<\/em>,\u03b8)=0.002\/(0.002+0.00016)\u22480.926<\/p>\n\n\n\n

\u548c<\/p>\n\n\n\n

P(S<\/em>2|T;D<\/em>,\u03b8)=0.00016\/(0.002+0.00016)\u22480.074<\/p>\n\n\n\n

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<\/p>\n\n\n\n

2.TopWORDS\u6d41\u7a0b\u4ecb\u7ecd<\/span><\/p>\n\n\n\n

\"\"
\u56fe1\u00a0 TopWORDS\u65b9\u6cd5\u7684\u6d41\u7a0b\u56fe<\/figcaption><\/figure><\/div>\n\n\n\n

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<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

<\/p>\n\n\n\n

3.TopWORDS\u7684\u4f18\u52bf\u548c\u7279\u70b9<\/span><\/p>\n\n\n\n

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<\/p>\n\n\n\n

\u4e8c\u3001\u5b9e\u4f8b\u5206\u6790<\/span><\/strong><\/p>\n\n\n\n

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<\/p>\n\n\n\n

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<\/p>\n\n\n\n

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\u603b\u7ed3<\/span><\/strong><\/p>\n\n\n\n

\u672c\u6587\u4ecb\u7ecd\u4e86\u57fa\u4e8e\u65e0\u76d1\u7763\u5b66\u4e60\u7684\u4e2d\u6587\u8bcd\u8bed\u53d1\u73b0\u548c\u5206\u8bcd\u65b9\u6cd5TopWORDS\u4ee5\u53ca\u53e6\u5916\u4e94\u79cd\u57fa\u4e8e\u6709\u76d1\u7763\u5b66\u4e60\u7684\u7ade\u4e89\u65b9\u6cd5\uff0c\u5e76\u5c06\u8fd9\u4e9b\u65b9\u6cd5\u5e94\u7528\u4e8e\u4e24\u90e8\u53e4\u7c4d\u300a\u6c49\u4e66\u300b\u548c\u300a\u660e\u53f2\u300b\u7684\u8bcd\u8bed\u53d1\u73b0\u548c\u4e13\u540d\u8bc6\u522b\u4e2d\u3002\u901a\u8fc7\u4e0e\u6743\u5a01\u7684\u4eba\u540d\u3001\u5730\u540d\u7d22\u5f15\u76f8\u6bd4\u8f83\uff0c\u6211\u4eec\u53d1\u73b0TopWORDS\u65b9\u6cd5\u5728\u53e4\u7c4d\u4e13\u540d\u8bc6\u522b\u4e2d\u6709\u7740\u663e\u8457\u7684\u4f18\u52bf:\u5bf9\u4e8e\u5b9e\u4f53\u9891\u6570\u5927\u4e8e\u7b49\u4e8e3\u7684\u4eba\u540d\u6216\u5730\u540d\uff0c\u8d85\u8fc7\u4e8695%\u7684\u6316\u6398\u7387;\u5bf9\u4e8e\u5b9e\u4f53\u9891\u6570\u5927\u4e8e\u7b49\u4e8e10\u7684\u4eba\u540d\u6216\u5730\u540d\uff0c\u8d85\u8fc7\u4e8699.6%\u7684\u6316\u6398\u7387;\u6781\u5c11\u6570\u672a\u76f4\u63a5\u8bc6\u522b\u7684\u4eba\u540d\uff0c\u7b97\u6cd5\u5219\u627e\u5230\u4e86\u5305\u542b\u8be5\u8bcd\u8bed\u7684\u5177\u6709\u66f4\u8be6\u7ec6\u4fe1\u606f\u7684\u77ed\u8bed\u3002\u76f8\u5173\u7ed3\u679c\u4e00\u81f4\u5730\u4f18\u4e8e\u5176\u4ed6\u65b9\u6cd5\uff0c\u5728\u5927\u591a\u6570\u60c5\u51b5\u4e0b\u9ad8\u51fa10\u4e2a\u767e\u5206\u70b9\u5de6\u53f3\uff0c\u5728\u6bd4\u8f83\u56f0\u96be\u7684\u300a\u6c49\u4e66\u300b\u4eba\u540d\u6316\u6398\u4efb\u52a1\u4e0a\u5219\u6709\u8d85\u8fc720\u4e2a\u767e\u5206\u70b9\u7684\u6027\u80fd\u63d0\u5347\u3002<\/p>\n\n\n\n

\u4e0a\u8ff0\u7ed3\u679c\u8bf4\u660e:\u5728\u6ca1\u6709\u8f93\u5165\u8bcd\u5e93\u548c\u8bad\u7ec3\u8bed\u6599\u5e93\u7684\u60c5\u51b5\u4e0b\uff0cTopWORDS\u65b9\u6cd5\u53ef\u4ee5\u9ad8\u6548\u51c6\u786e\u5730\u4ece\u53e4\u6587\u732e\u4e2d\u8bc6\u522b\u4e13\u6709\u540d\u8bcd\u3002\u5728\u76ee\u6807\u6587\u732e\u4e13\u540d\u7d22\u5f15\u5c1a\u672a\u5efa\u7acb\u7684\u60c5\u51b5\u4e0b\uff0c\u4e0a\u8ff0\u57fa\u4e8eTopWORDS\u65b9\u6cd5\u7684\u5206\u6790\u7b56\u7565\u53ef\u4ee5\u5728\u5c11\u91cf\u4eba\u5de5\u5e72\u9884\u4e0b\u5feb\u901f\u6784\u5efa\u4e13\u540d\u7d22\u5f15\uff0c\u4ece\u800c\u6781\u5927\u5730\u63d0\u9ad8\u4eba\u6587\u7814\u7a76\u7684\u5de5\u4f5c\u6548\u7387\u3002\u5982\u679c\u6211\u4eec\u628a\u89c6\u91ce\u653e\u5bbd\uff0c\u4ee5TopWORDS\u65b9\u6cd5\u4ea7\u751f\u7684\u7ed3\u679c\u4e3a\u57fa\u7840\uff0c\u751f\u6210\u76f8\u5e94\u7684\u8bcd\u5e93\uff0c\u5e76\u505a\u51fa\u5fc5\u8981\u7684\u6574\u7406\uff0c\u6216\u8bb8\u80fd\u4e3a\u89e3\u51b3\u53e4\u6c49\u8bed\u81ea\u7136\u8bed\u8a00\u5904\u7406(NLP)\u7684\u74f6\u9888\u2014\u2014\u6587\u8a00\u5206\u8bcd\u6280\u672f\u63d0\u4f9b\u4e86\u4e00\u6761\u6709\u4ef7\u503c\u7684\u901a\u9014\u3002<\/p>\n\n\n\n

(\u8877\u5fc3\u611f\u8c22\u5317\u4eac\u667a\u6e90\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u5bf9\u672c\u6587\u7684\u652f\u6301\u3002)<\/span><\/p>\n\n\n\n

—————————————————————————————————————————————————————————————<\/span><\/p>\n\n\n\n

Recognition of Technical Terms in Ancient Chinese Texts via TopWORDS Method:Take The Book of the Han and The History of the Ming as Examples<\/span><\/strong><\/p>\n\n\n\n


Xu Jiaze, Pan Changzai, He Lili, Wang Hongsu, Zhang Liwei, Deng Ke
<\/p>\n\n\n\n

Abstract:<\/strong>\u00a0The appearance of large amounts of digital ancient Chinese text resources creates great demands for corresponding analysis tools. As an unsupervised Chinese text analysis method based on a statistical model, TopWORDS has application prospects for word discovery and information extraction in ancient Chinese texts. This paper introduces the basic principle, working process, characteristics and advantages of the TopWORDS method, applies it to the word discovery of two ancient books, The Book of the Han and The History of the Ming, and quantitatively evaluates and compares the capabilities of extracting technical terms in ancient documents based on name and address indexes as standards. Related results prove that TopWORDS has a strong ability to extract technical terms in ancient Chinese text analysis, and has the potential to play an important role in recognizing out-of-vocabulary technical terms and rapidly constructing indexes of proper nouns.
Keywords:<\/strong>\u00a0Word Discovery; Unsupervised Learning; Chinese Word Segmentation<\/p>\n\n\n\n

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\u7f16 \u8f91  | \u4e25\u7a0b<\/p>\n\n\n\n

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*\u672c\u6587\u4e3a\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1(11771242)\u548c\u56fd\u5bb6\u793e\u4f1a\u79d1\u5b66\u57fa\u91d1\u91cd\u5927\u9879\u76ee\u201c\u57fa\u4e8e\u5927\u6570\u636e\u6280\u672f\u7684\u53e4\u5178\u6587\u5b66\u7ecf\u5178\u6587\u672c\u5206\u6790\u4e0e\u7814\u7a76\u201d(18ZDA238)\u9636\u6bb5\u6027\u6210\u679c\u3002<\/span><\/p>\n\n\n\n

\u6ce8\u91ca\uff1a<\/span><\/strong><\/p>\n\n\n\n

[1]\u9646\u5b87\u6770\u3001\u8bb8\u946b\u3001\u90ed\u91d1\u9f99:\u300a\u6587\u672c\u6316\u6398\u5728\u4eba\u6587\u793e\u4f1a\u79d1\u5b66\u7814\u7a76\u4e2d\u7684\u5178\u578b\u5e94\u7528\u8ff0\u8bc4\u300b\uff0c\u300a\u56fe\u4e66\u60c5\u62a5\u5de5\u4f5c\u300b2012\u5e74\u7b2c8\u671f\u3002<\/p>\n\n\n\n

[2]Fuchun Peng, Fangfang Feng and Andrew McCallum\u201c, Chinese Segmentation and New Word Detection Using Conditional Random Fields,\u201din Proceedings of the 20th International Conference on Computational Linguistics(COLING), 2004, pp. 562-568;\u6881\u793e\u4f1a\u3001\u9648\u5c0f\u8377:\u300a\u5148\u79e6\u6587\u732e\u3008\u5b5f\u5b50\u3009\u81ea\u52a8\u5206\u8bcd\u65b9\u6cd5\u7814\u7a76\u300b\uff0c\u300a\u5357\u4eac\u5e08\u8303\u5927\u5b66\u6587\u5b66\u9662\u5b66\u62a5\u300b2013\u5e74\u7b2c3\u671f\u3002<\/p>\n\n\n\n

[3]T. Xie, B. Wu and B. Wang\u201c, New Word Detection in Ancient Chinese Literature,\u201dAsia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, Springer, Cham, 2017;\u5218\u6631\u5f64\u7b49:\u300a\u57fa\u4e8e\u53e4\u6c49\u8bed\u8bed\u6599\u7684\u65b0\u8bcd\u53d1\u73b0\u65b9\u6cd5\u300b\uff0c\u300a\u4e2d\u6587\u4fe1\u606f\u5b66\u62a5\u300b2019\u5e74\u7b2c1\u671f\u3002<\/p>\n\n\n\n

[4]Z. Liang et al.\u201c, Chinese New Words Detection Using Mutual Information,\u201dTrustworthy Computing and Services, vol. 320 (Berlin, Heidelberg: Springer Berlin Heidelberg, 2013), pp. 341-348.<\/p>\n\n\n\n

[5]\u6bb5\u78ca\u3001\u97e9\u82b3\u3001\u5b8b\u7ee7\u534e:\u300a\u53e4\u6c49\u8bed\u53cc\u5b57\u8bcd\u81ea\u52a8\u83b7\u53d6\u65b9\u6cd5\u7684\u6bd4\u8f83\u4e0e\u5206\u6790\u300b\uff0c\u300a\u4e2d\u6587\u4fe1\u606f\u5b66\u62a5\u300b2012\u5e74\u7b2c4\u671f\uff0c\u7b2c34\u201443\u9875\u3002<\/p>\n\n\n\n

[6]Ao Chen and Maosong Sun,\u201cDomain-Specific New Words Detection in Chinese,\u201din Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), Vancouver, Canada: Association for Computational Linguistics, 2017, pp. 44-53.<\/p>\n\n\n\n

[7]\u674e\u658c\u3001\u9648\u5c0f\u8377:\u300a\u9762\u5411\u4e2d\u6587\u964c\u751f\u6587\u672c\u7684\u4eba\u673a\u4ea4\u4e92\u5f0f\u5206\u8bcd\u65b9\u6cd5\u300b\uff0c\u300a\u4e2d\u6587\u4fe1\u606f\u5b66\u62a5\u300b2007\u5e74\u7b2c3\u671f\u3002<\/p>\n\n\n\n

[8]\u6b27\u9633\u5251:\u300a\u9762\u5411\u6570\u5b57\u4eba\u6587\u7814\u7a76\u7684\u5927\u89c4\u6a21\u53e4\u7c4d\u6587\u672c\u53ef\u89c6\u5316\u5206\u6790\u4e0e\u6316\u6398\u300b\uff0c\u300a\u4e2d\u56fd\u56fe\u4e66\u9986\u5b66\u62a5\u300b2016\u5e74\u7b2c2\u671f\u3002<\/p>\n\n\n\n

[9]Ke Deng, Peter K. Bol, Kate J. Li and Jun S. Liu\u201c, On the Unsupervised Analysis of Domain-Specific Chinese Texts, \u201dProceedings of the National Academy of Sciences, vol. 113, no. 22, 2016, pp. 6154-6159.<\/p>\n\n\n\n

[10]Ke Deng, Peter K. Bol, Kate J. Li and Jun S. Liu\u201c, On the Unsupervised Analysis of Domain-Specific Chinese Texts, \u201dProceedings of the National Academy of Sciences, vol. 113, no. 22, 2016, pp. 6154-6159.<\/p>\n\n\n\n

[11]\u5b59\u8302\u677e\u7b49:\u201cTHULAC:\u4e00\u4e2a\u9ad8\u6548\u7684\u4e2d\u6587\u8bcd\u6cd5\u5206\u6790\u5de5\u5177\u5305\u201d(2016)\uff0chttp:\/\/thulac.thunlp.org\/\u3002<\/p>\n\n\n\n

[12]Ruixuan Luo et al.,\u201cPKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation, \u201d arXiv:1906.11455 [cs], 27 Jun. 2019, http:\/\/arxiv.org\/abs\/1906.11455.  <\/p>\n\n\n\n

[13]Wanxiang Che, Zhenghua Li, Ting Liu\u201c, LTP: A Chinese Language Technology Platform, \u201din Proceedings of the Coling(2010)Demonstrations, Beijing, China, 2010, pp. 13-16.<\/p>\n\n\n\n

[14]Manning, Christopher D., et al.,\u201cThe Stanford CoreNLP Natural Language Processing Toolkit,\u201d Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations 2014.<\/p>\n\n\n\n

[15]See https:\/\/github.com\/fxsjy\/jieba.<\/p>\n\n\n\n

[16]\u7f57\u76db\u82ac\u3001\u5b59\u8302\u677e:\u300a\u57fa\u4e8e\u5b57\u4e32\u5185\u90e8\u7ed3\u5408\u7d27\u5bc6\u5ea6\u7684\u6c49\u8bed\u81ea\u52a8\u62bd\u8bcd\u5b9e\u9a8c\u7814\u7a76\u300b\uff0c\u300a\u4e2d\u6587\u4fe1\u606f\u5b66\u62a5\u300b2003\u5e74\u7b2c3\u671f;\u5b59\u8302\u677e\uff0c\u8096\u660e\u3001\u90b9\u5609\u5f66:\u300a\u57fa\u4e8e\u65e0\u6307\u5bfc\u5b66\u4e60\u7b56\u7565\u7684\u65e0\u8bcd\u8868\u6761\u4ef6\u4e0b\u7684\u6c49\u8bed\u81ea\u52a8\u5206\u8bcd\u300b\uff0c\u300a\u8ba1\u7b97\u673a\u5b66\u62a5\u300b2004\u5e74\u7b2c6\u671f\u3002<\/p>\n\n\n\n

[17]Ke Deng, Peter K. Bol, Kate J. Li and Jun S. Liu\u201c, On the Unsupervised Analysis of Domain-Specific Chinese Texts,\u201dProceedings of the National Academy of Sciences, vol. 113, no. 22, 2016, pp. 6154-6159.<\/p>\n\n\n\n

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