{
    "info": {
        "author": "Kechen Lu",
        "author_email": "colearolu@icloud.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Information Analysis"
        ],
        "description": "=======\nHuhuSeg\n=======\n\nHuhuSeg\u662f\u4e00\u4e2a\u57fa\u4e8e **MMSEG** [1]_ \u7684\u56db\u79cd\u6d88\u6b67\u89c4\u5219\u548c\u6700\u5927\u5339\u914d\u65b9\u5f0f\u7684\u7b80\u5355\u4e2d\u6587\u5206\u8bcd\u5668\uff0c\u53ea\u6709\u6570\u767e\u884c\u4ee3\u7801\u3002  \n\nSimple Chinese segmentor based on the four ambiguity-resolving rules by **MMSEG** [1]_ and examples.\n\n\u540c\u65f6HuhuSeg\u5b9e\u73b0\u4e86\u4e00\u4e2a\u7b80\u5355\u4f46\u662f\u975e\u5e38\u9ad8\u6548\u7684\u8bcd\u56fe\u751f\u6210\u65b9\u5f0f\uff0c\u7531 **HanLP** [3]_ \u7684\u542f\u53d1\u800c\u6765\u3002\u540c\u65f6\u6838\u5fc3\u8bcd\u5178\u76f4\u63a5\u4f7f\u7528\u4e86**jieba** [2]_ \u7684\u8bcd\u9891\u8bcd\u5178\u3002  \n\nHuhuSeg implemented a simple but graceful words-gram generation enlightened by **HanLP** [3]_. And the dictionary(including the words tag and frequency) was included from **jieba** [2]_. \u00a0\n\nChange Log\n----------\n\n**0.4.29** :\n\n1. Add hotspot words weight computing.\n2. Add extraction of topic-phrase for news based on hmm.\n3. Fix bugs.\n\n**0.3.26** :\n\n1. Add support of named entity recognition based on 2-gram hmm model\n2. Add support of text centroid clustering\n3. Update the texts code\n\n**0.3.07** :\n\n1. Add support of Bag-of-Words model to compute the similarity\n2. Add weight_similarity function for text clustering\n3. Update the texts code\n\nHow-to use\n----------\n\nInstallation\n~~~~~~~~~~~~\n\n::\n\n    pip3 install HuhuSeg\n\nSegmentation\n~~~~~~~~~~~~\n\n\u5982\u4e0b\u4ee3\u7801\u4e3a\u5206\u8bcd\u5668\u7684\u4f7f\u7528\u65b9\u5f0f\uff1a\n\nWe can just try to segment the Chinese texts like this : \u00a0\n\n.. code:: python\n\n    from huhu_seg.segmentor import Segmentor\n\n    s = Segmentor('\u674e\u667a\u4f1f\u9ad8\u9ad8\u5174\u5174\u548c\u738b\u6653\u8587\u51fa\u53bb\u73a9\u3002', hmm_config = True)\n    tokens = s.gen_tokens()\n    for item in tokens:\n        print(str(item))\n\nAnd the output is :\n\n::\n\n    [frequency 3 | nr | length 3] \u674e\u667a\u4f1f\n    [frequency 119 | ns | length 4] \u9ad8\u9ad8\u5174\u5174\n    [frequency 555815 | c | length 1] \u548c\n    [frequency 3 | nr | length 3] \u738b\u6653\u8587\n    [frequency 3 | n | length 3] \u51fa\u53bb\u73a9\n    [frequency 0 | wj | length 1] \u3002\n\n\nKeywords Extraction\n~~~~~~~~~~~~~~~~~~~\n\nTF-IDF Keywords Extraction\n^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nHuhuSeg\u57fa\u4e8e **TF-IDF** [4]_ \u7b97\u6cd5\u5b9e\u73b0\u4e86\u4e00\u4e2a\u5173\u952e\u8bcd\u63d0\u53d6\u5668\uff0cIDF\u8bcd\u9891\u6a21\u578b\u6765\u6e90\u4e8e\u9488\u5bf9\u63a5\u8fd1100\u4e07\u6761\u7684wiki\u4e2d\u6587\u8bcd\u6761\u7684\u5206\u8bcd\u7edf\u8ba1\uff0c\u5982\u4e0b\u4e3a\u4f7f\u7528\u65b9\u5f0f\uff1a \n\nNow HuhuSeg supports the keywords extraction based on the **TF-IDF** [4]_ . Just have a try like : \u00a0\n\n.. code:: python\n\n    from huhu_seg.tfidf import TFIDF\n\n    k = TFIDF('\u7a0b\u5e8f\u5458(\u82f1\u6587Programmer)\u662f\u4ece\u4e8b\u7a0b\u5e8f\u5f00\u53d1\u3001\u7ef4\u62a4\u7684\u4e13\u4e1a\u4eba\u5458\u3002\u4e00\u822c\u5c06\u7a0b\u5e8f\u5458\u5206\u4e3a\u7a0b\u5e8f\u8bbe\u8ba1\u4eba\u5458\u548c\u7a0b\u5e8f\u7f16\u7801\u4eba\u5458\uff0c\u4f46\u4e24\u8005\u7684\u754c\u9650\u5e76\u4e0d\u975e\u5e38\u6e05\u695a\uff0c\u7279\u522b\u662f\u5728\u4e2d\u56fd\u3002\u8f6f\u4ef6\u4ece\u4e1a\u4eba\u5458\u5206\u4e3a\u521d\u7ea7\u7a0b\u5e8f\u5458\u3001\u9ad8\u7ea7\u7a0b\u5e8f\u5458\u3001\u7cfb\u7edf\u5206\u6790\u5458\u548c\u9879\u76ee\u7ecf\u7406\u56db\u5927\u7c7b\u3002')\n    list = k.extract_kw()\n    for word, freq in list :\n        print('[%s %f]' % (word, freq))\n\nOutput is :\n\n::\n\n    [\u7a0b\u5e8f\u5458 0.735660]\n    [\u4eba\u5458 0.305123]\n    [\u7cfb\u7edf\u5206\u6790\u5458 0.300264]\n    [\u7a0b\u5e8f\u5f00\u53d1 0.232286]\n    [\u9879\u76ee\u7ecf\u7406 0.231476]\n\nTextRank Keywords Extraction\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\u5728TF-IDF\u5b9e\u73b0\u7684\u5173\u952e\u8bcd\u63d0\u53d6\u4e4b\u5916\uff0c\u8fd9\u91cc\u8fd8\u5b9e\u73b0\u4e86\u57fa\u4e8e **TextRank** [5]_ \u7684\u63d0\u53d6\u7b97\u6cd5\uff0c\u4e0d\u4f9d\u8d56\u4e8e\u5e9e\u5927\u7684IDF\u6a21\u578b\uff0c\u800c\u662f\u8bd5\u56fe\u5728\u6587\u672c\u4e2d\u8bcd\u8bed\u7684\u5171\u73b0\u5173\u7cfb\u56fe\u91cc\u627e\u5230\u88abRank\u6700\u9ad8\u7684\u8bcd\u8bed\u3002\u5f53\u7136\uff0c\u9664\u6b64\u4e4b\u5916\uff0c\u8fd9\u91cc\u7684\u4ee3\u7801\u5b9e\u73b0\u7684\u5173\u952e\u8bcd\u63d0\u53d6\u8fd8\u4f7f\u7528\u4e86\u4e00\u4e2a\u5c0ftrick\uff0c\u5728\u901a\u8fc7TextRank\u63d0\u53d6\u5b8c\u5173\u952e\u8bcd\u4e4b\u540e\uff0c\u4f1a\u518d\u6b21\u626b\u63cf\u6587\u672c\uff0c\u627e\u5230top\u5173\u952e\u8bcd\u4e2d\u662f\u5426\u6709\u90bb\u63a5\u8bcd\u53ef\u4ee5\u7ec4\u6210\u77ed\u8bed\uff0c\u5982\u4e0b\u9762\u7684\u63d0\u53d6\u201c\u65c5\u884c\u9752\u86d9\u201d\u548c\u201c\u5f00\u53d1\u6e38\u620f\u201d\u5373\u662f\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\u63d0\u53d6\u51fa\u6765\u7684\u3002\n\nBy **TextRank** [5]_ , we can escape those huge and overwhelming IDF dictionaries, and try to find the relationship of words between the co-occuring gragh. As you can see in this output, we have a little trick to extract the better keywords with meaningful semantics: the extractor may scan the whole text to find if there are top keywords could construct the phrase. If it is true, we select them and build new keywords table. In the test, it seems to have the effective and better results. \u00a0\n\n.. code:: python\n\n    from huhu_seg.textrank import TextRank\n\n    t = TextRank(\"\"\"\u300a\u65c5\u884c\u9752\u86d9\u300b\u76ee\u524d\u4ecd\u662fApp Store\u4e2d\u56fd\u533a\u514d\u8d39\u6e38\u620f\u4e0b\u8f7d\u699c\u699c\u9996\u3002\n    \u4e00\u6b3e\u653e\u7f6e\u7c7b\u4f11\u95f2\u624b\u6e38\uff0c\u5728\u6ca1\u6709\u6c49\u5316\u7248\u7684\u60c5\u51b5\u4e0b\uff0c\u6253\u8d25\u4e00\u4f17\u8bd5\u56fe\u5c06\u73a9\u5bb6\u62fd\u5165\u6c89\u8ff7\u7684\u201c\u809d\u7cfb\u6e38\u620f\u201d\uff0c\u8fbe\u6210\u4e86\u4e00\u4e2a\u4e0d\u5927\u4e0d\u5c0f\u7684\u5947\u8ff9\u3002\n    \u300a\u65c5\u884c\u9752\u86d9\u300b\u7684\u73a9\u6cd5\u6781\u5176\u7b80\u5355\uff0c\u73a9\u5bb6\u53ea\u9700\u91c7\u96c6\u5ead\u9662\u91cc\u7684\u56db\u53f6\u8349\u4e3a\u9752\u86d9\u8d2d\u7f6e\u65c5\u884c\u4f7f\u7528\u7684\u4fbf\u5f53\u3001\u9053\u5177\u3001\u62a4\u8eab\u7b26\u4e09\u6837\u7269\u54c1\uff0c\u4e3a\u65c5\u884c\u86d9\u505a\u597d\u51fa\u95e8\u65c5\u884c\u7684\u51c6\u5907\u5c31\u53ef\u4ee5\u4e86\u3002\u6e38\u620f\u91cc\u7684\u7b49\u5f85\u591a\u8fc7\u64cd\u4f5c\uff0c\u4e5f\u6709\u4eba\u628a\u5b83\u5f53\u6210\u5f53\u4e0b\u6700\u706b\u7684\u201c\u4f5b\u7cfb\u201d\u8bf4\u6cd5\u91cc\u7684\u201c\u4f5b\u7cfb\u6e38\u620f\u201d\u3002\n    \u5bf9\u300a\u65c5\u884c\u9752\u86d9\u300b\u7684\u5236\u65b9Hit-Point\u6765\u8bf4\uff0c\u8d70\u8fdb\u821e\u53f0\u4e2d\u592e\uff0c\u66dd\u5149\u5728\u805a\u5149\u706f\u4e0b\uff0c\u5374\u662f\u4e00\u79cd\u65e0\u6765\u7531\u7684\u614c\u4e71\u3002\u201c\u592a\u610f\u5916\u201d\u662f\u4ed6\u4eec\u5411\u5916\u754c\u9648\u8ff0\u611f\u53d7\u65f6\uff0c\u6700\u5e38\u63d0\u5230\u7684\u4e00\u4e2a\u8bcd\u8bed\u3002\n    \u521b\u7acb\u4e8e2007\u5e74\u7684Hit-Point\uff0c\u4e3b\u8981\u4ece\u4e8b\u4f11\u95f2\u624b\u673a\u6e38\u620f\u5f00\u53d1\uff0c\u4f8b\u5982\u4ed6\u4eec\u7684\u300a\u732b\u54aa\u540e\u9662\u300b\uff0c\u8fd9\u6b3e\u6e38\u620f\u8ba9\u73a9\u5bb6\u5728\u5ead\u9662\u91cc\u6446\u653e\u5404\u79cd\u9053\u5177\u6765\u5438\u5f15\u5404\u5f0f\u5404\u6837\u7684\u732b\u54aa\u3002\u5728\u5341\u5e74\u4e2d\uff0cHit-Point\u5171\u5f00\u53d1\u4e86\u7ea630\u6b3e\u6e38\u620f\u3002\n    \u5c3d\u7ba1\u300a\u65c5\u884c\u9752\u86d9\u300b\u662fHit-Point\u7684\u6700\u65b0\u6e38\u620f\uff0c\u4f46\u5b9e\u9645\u4e0a\uff0c\u5f00\u53d1\u8005\u6ca1\u6709\u4e3a\u8fd9\u6b3e\u6e38\u620f\u8d4b\u4e88\u592a\u591a\u610f\u4e49\uff0c\u6309\u7167Hit-Point\u7684\u8bf4\u6cd5\uff0c\u5728\u5f00\u53d1\u6e38\u620f\u65f6\uff0c\u4ed6\u4eec\u4ec5\u7b80\u5355\u8bbe\u7f6e\u4e86\u4e00\u4e2a\u201c10\u5c81\u523030\u5c81\u7684\u5973\u6027\u201d\u7684\u76ee\u6807\u5ba2\u6237\u8303\u56f4\u3002\n    \u800c\u73b0\u5728\uff0c\u300a\u65c5\u884c\u9752\u86d9\u300b\u8986\u76d6\u73a9\u5bb6\u7fa4\u4f53\u5df2\u7ecf\u5927\u5927\u8d85\u51faHit-Point\u8bbe\u5b9a\u76ee\u6807\uff0c\u5728\u4e2d\u56fd\u66f4\u662f\u83b7\u5f97\u4e86\u7206\u53d1\u6027\u589e\u957f\u3002Hit-Point\u544a\u8bc9\u754c\u9762\u65b0\u95fb\u8bb0\u8005\uff0c\u622a\u81f31\u670826\u65e5\uff0c\u300a\u65c5\u884c\u9752\u86d9\u300b\u4e0b\u8f7d\u603b\u91cf\u5df2\u8fbe\u52301100\u4e07\uff0c\u76ee\u524d\u8fd9\u4e2a\u6570\u5b57\u4ecd\u5728\u8fc5\u901f\u589e\u957f\u3002\u6839\u636e\u65e5\u672c\u5a92\u4f53\u62a5\u9053\uff0c\u5728App Store\u7684\u4e0b\u8f7d\u603b\u91cf\u4e2d\uff0c\u4e2d\u56fd\u536095%\uff0c\u65e5\u672c\u4ec5\u67092%\u3002\n    \u201c\u6211\u4eec\u4e00\u76f4\u5728\u52aa\u529b\u8bbe\u8ba1\u548c\u5f00\u53d1\u6e38\u620f\uff0c\u5e76\u671f\u671b\u5b83\u4eec\u80fd\u88ab\u4e16\u754c\u8303\u56f4\u5185\u7684\u73a9\u5bb6\u6240\u63a5\u53d7\uff0c\u4f46\u300a\u65c5\u884c\u9752\u86d9\u300b\u5728\u4e2d\u56fd\u83b7\u5f97\u5982\u6b64\u5927\u8303\u56f4\u7684\u6d41\u884c\uff0c\u8fd8\u662f\u8d85\u8fc7\u4e86\u6211\u4eec\u60f3\u8c61\u3002\u201d\u4e00\u4f4dHit-Point\u8d1f\u8d23\u4eba\u544a\u8bc9\u754c\u9762\u65b0\u95fb\u8bb0\u8005\uff0c\u4ed6\u4eec\u6ca1\u6709\u8fdb\u884c\u4efb\u4f55\u6e38\u620f\u63a8\u5e7f\u3002\n    \u201c\u4e5f\u8bb8\u662f\u6e38\u620f\u975e\u5e38\u7b80\u5355\uff0c\u4eba\u4eec\u4f1a\u60f3\u4e3a\u4ec0\u4e48\u4e0d\u4e0b\u8f7d\u8bd5\u8bd5\u5462\uff1f\u800c\u4e14\u901a\u8fc7\u793e\u4ea4\u5a92\u4f53\u548c\u53e3\u5934\u4f20\u64ad\uff0c\u8fd9\u79cd\u8fde\u9501\u53cd\u5e94\u4e00\u4e0b\u5b50\u5438\u5f15\u4e86\u5f88\u591a\u73a9\u5bb6\u3002\u201dHit-Point\u76f8\u5173\u8d1f\u8d23\u4eba\u731c\u6d4b\u6e38\u620f\u6210\u529f\u7684\u539f\u56e0\u65f6\u8bf4\u9053\u3002\n    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Store\u7545\u9500\u6392\u540d\u7b2c21\uff0c\u8d85\u8fc7\u300a\u9634\u9633\u5e08\u300b\u3001\u300a\u8352\u91ce\u884c\u52a8\u300b\u7b49\u624b\u6e38\u3002\n    \u201c\u300a\u65c5\u884c\u9752\u86d9\u300b\u88ab\u8bbe\u5b9a\u4e3a\u4e00\u6b3e\u53ef\u4ee5\u57fa\u672c\u514d\u8d39\u73a9\u7684\u6e38\u620f\uff0c\u4f46\u4f3c\u4e4e\u6bd4\u6211\u4eec\u60f3\u8c61\u4e2d\u6709\u66f4\u591a\u7528\u6237\u4f7f\u7528\u4e86\u6e38\u620f\u7684\u5185\u8d2d\uff0c\u201dHit-Point\u8d1f\u8d23\u4eba\u544a\u8bc9\u754c\u9762\u8bb0\u8005\uff0c\u201c\u6839\u636e\u6211\u4eec\u7edf\u8ba1\uff0c\u5728\u6e38\u620f\u7684\u65e5\u6d3b\u8dc3\u7528\u6237\u4e2d\uff0c\u7ea6\u67093%-8%\u9009\u62e9\u4e86\u5185\u8d2d\u3002\u201d\n    \u5b9e\u9645\u4e0a\uff0c\u4f34\u968f\u6e38\u620f\u7684\u706b\u7206\uff0c\u5404\u79cd\u5c71\u5be8\u7248\u300a\u65c5\u884c\u9752\u86d9\u300b\u5df2\u7ecf\u5728\u5e94\u7528\u5e02\u573a\u6cdb\u6ee5\u3002\u6b63\u7248\u300a\u65c5\u884c\u9752\u86d9\u300b\u5728App Store\u91cc\u663e\u793a\u7684\u540d\u5b57\u4e3a\u300a\u65c5\u304b\u3048\u308b\u300b\uff0c\u5236\u4f5c\u516c\u53f8\u4e3aHit-Point Co,.Ltd\uff0c\u5728App Store\u91cc\u8bc4\u52064.3\u3002\u800c\u6b64\u524d\uff0c\u73a9\u5bb6\u82e5\u5728App Store\u641c\u7d22\u4e2d\u6587\u201c\u65c5\u884c\u9752\u86d9\u201d\uff0c\u5219\u4f1a\u51fa\u73b0\u4e00\u6b3e\u6536\u8d3930\u5143\uff0c\u540d\u4e3a\u201c\u65c5\u884c\u9752\u86d9.\u201d\u7684\u4eff\u5236\u7248\u6e38\u620f\uff0c\u6e38\u620f\u73a9\u6cd5\u7c7b\u4f3c\u5fae\u4fe1\u5c0f\u6e38\u620f\u201c\u8df3\u4e00\u8df3\u201d\uff0c\u4f46\u8be5\u5e94\u7528\u5f00\u53d1\u8005\u663e\u793a\u201cSong Yang\u201d\u3002\u76ee\u524d\uff0c\u8be5\u6e38\u620f\u5df2\u7ecf\u88ab\u82f9\u679c\u4e0b\u67b6\u3002\n    \u5bf9\u4e8e\u9891\u7e41\u51fa\u73b0\u7684\u5c71\u5be8\u7248\u6e38\u620f\uff0c\u4ee5\u53ca\u975e\u6388\u6743\u63d0\u4f9b\u7684\u76d7\u7248\u300a\u65c5\u884c\u9752\u86d9\u300b\uff0cHit-Point\u66f4\u591a\u7684\u662f\u4e00\u79cd\u65e0\u5948\uff0c\u201c\u76d7\u7248\u5b58\u5728\u5bf9\u6211\u4eec\u6765\u8bf4\u662f\u4e00\u4e2a\u5f88\u96be\u8fc7\u7684\u95ee\u9898\uff0c\u5f53\u7528\u6237\u56e0\u4e3a\u76d7\u7248\u53d7\u5230\u4efb\u4f55\u635f\u5931\u65f6\uff0c\u6211\u4eec\u4f1a\u66f4\u52a0\u96be\u8fc7\uff0c\u5bf9\u6b64\u6211\u4eec\u8ba4\u4e3a\u6709\u5fc5\u8981\u91c7\u53d6\u4e00\u4e9b\u5bf9\u7b56\u3002\u4e0d\u8fc7\u9996\u5148\uff0c\u6211\u4eec\u5e0c\u671b\u521b\u9020\u4e00\u4e2a\u5411\u7528\u6237\u4f20\u9012\u6b63\u786e\u4fe1\u606f\u7684\u73af\u5883\u3002\u201d\n    Hit-Point\u5411\u754c\u9762\u8bb0\u8005\u900f\u9732\uff0c\u5bf9\u4e8e\u5c06\u300a\u65c5\u884c\u9752\u86d9\u300b\u6b63\u5f0f\u5f15\u5165\u4e2d\u56fd\u7684\u95ee\u9898\uff0c\u4ed6\u4eec\u5df2\u7ecf\u6536\u5230\u4e00\u4e9b\u4e2d\u56fd\u516c\u53f8\u7684\u5408\u4f5c\u63d0\u8bae\u5e76\u79ef\u6781\u8003\u8651\u4e2d\uff0c\u4f46\u8fd8\u6ca1\u6709\u8fbe\u5230\u8c08\u8bba\u7ec6\u8282\u7684\u9636\u6bb5\u3002\u5173\u4e8e\u300a\u65c5\u884c\u9752\u86d9\u300b\u7684\u4e2d\u56fd\u6587\u5316\uff0c\u8fd9\u5bb6\u516c\u53f8\u8868\u793a\u53ef\u80fd\u4f1a\u548c\u300a\u65c5\u884c\u9752\u86d9\u300b\u7684\u4ee3\u7406\u5408\u4f5c\u516c\u53f8\u4e00\u540c\u8ba8\u8bba\u3002\n    \u5bf9\u4e8e\u300a\u65c5\u884c\u9752\u86d9\u300b\u672a\u6765\u7684\u66f4\u65b0\uff0cHit-Point\u8868\u793a\uff0c\u5b83\u4eec\u9996\u5148\u5c06\u589e\u52a0\u9752\u86d9\u53ef\u53c2\u89c2\u5730\u70b9\u7684\u6570\u91cf\uff0c\u8fd9\u6837\uff0c\u9752\u86d9\u5c06\u4f1a\u53d1\u56de\u66f4\u591a\u7684\u65c5\u884c\u7167\u7247\u3002\u201c\u73a9\u5bb6\u7ed9\u6211\u4eec\u7684\u53cd\u9988\u975e\u5e38\u79ef\u6781\uff0c\u6bd4\u5982\u591a\u8bed\u8a00\u652f\u6301\u548c\u66f4\u591a\u7684\u65c5\u884c\u7167\u7247\u3002\u6211\u4eec\u4f1a\u5728\u4e0d\u65ad\u66f4\u65b0\u6e38\u620f\u7684\u540c\u65f6\u4e00\u8d77\u5904\u7406\u73a9\u5bb6\u7684\u8bf7\u6c42\u3002\u201d\n    \u968f\u7740\u300a\u65c5\u884c\u9752\u86d9\u300b\u5927\u706b\uff0cHit-Point\u4e5f\u62e5\u6709\u4e86\u300a\u732b\u54aa\u540e\u9662\u300b\u5916\u53c8\u4e00\u4e2a\u77e5\u540dIP\uff0c\u800c\u5bf9\u4e8e\u540e\u8005\uff0cHit-Point\u4e5f\u8fdb\u884c\u4e86\u76f8\u5f53\u6df1\u5165\u7684IP\u5f00\u53d1\uff0c\u6bd4\u5982\u5468\u8fb9\u300a\u732b\u54aa\u540e\u9662\u98df\u8c31\u300b\u4ee5\u53ca\u884d\u751f\u7535\u5f71\u3002\n    \u201c\u6211\u4eec\u6700\u521d\u6839\u672c\u65e0\u6cd5\u60f3\u8c61\u300a\u732b\u54aa\u540e\u9662\u300b\u4f1a\u88ab\u62cd\u6210\u7535\u5f71\u3002\u4f46\u4ece\u4eba\u7269\u8bbe\u5b9a\u548c\u4e16\u754c\u89c2\u6765\u770b\uff0c\u300a\u65c5\u884c\u9752\u86d9\u300b\u662f\u53ef\u4ee5\u901a\u8fc7\u5404\u79cd\u65b9\u5f0f\u5c55\u73b0\u7684\uff0c\u4f46\u8c01\u53c8\u77e5\u9053\u5b83\u4f1a\u5982\u4f55\u53d1\u5c55\u5462\uff1f\u201d\u5728\u300a\u65c5\u884c\u9752\u86d9\u300b\u884d\u751f\u54c1\u5f00\u53d1\u65b9\u9762\uff0cHit-Point\u8868\u793a\uff0c\u5982\u6709\u673a\u4f1a\uff0c\u4e00\u5b9a\u4f1a\u8fdb\u884c\u76f8\u5173\u7814\u7a76\u3002\n    \u636eHit-Point\u8d1f\u8d23\u4eba\u4ecb\u7ecd\uff0cHit-Point\u5185\u90e8\u6709\u591a\u4e2a\u5c0f\u7ec4\uff0c\u8d1f\u8d23\u5f00\u53d1\u4e0d\u540c\u7684\u6e38\u620f\u3002\u201c\u4ece\u8fd9\u65b9\u9762\u770b\uff0c\u6211\u4e0d\u80fd\u786e\u5b9a\u516c\u53f8\u7684\u53d1\u5c55\u65b9\u5411\uff0c\u4f46\u7b80\u5355\u7684\u6e38\u620f\u73a9\u6cd5\u786e\u5b9e\u662f\u6211\u4eec\u5438\u5f15\u5e7f\u6cdb\u73a9\u5bb6\u7fa4\u4f53\u7684\u91cd\u8981\u7406\u5ff5\u3002\u5728\u601d\u8003\u672a\u6765\u7684\u6e38\u620f\u5236\u4f5c\u65b9\u5411\u65f6\uff0c\u8fd9\u4f1a\u662f\u6211\u4eec\u7684\u91cd\u8981\u8003\u8651\u70b9\u3002\u201d\u4e0a\u8ff0\u8d1f\u8d23\u4eba\u8868\u793a\u3002\n    \u4ee5\u4e0b\u4e3a\u91c7\u8bbf\u6458\u8981\uff1a\n    \u754c\u9762\uff1a\u300a\u9752\u86d9\u65c5\u884c\u300b\u4f55\u65f6\u4f1a\u63d0\u4f9b\u4e2d\u6587\u5316\u7684\u7248\u672c\uff1f\u6709\u6ca1\u6709\u4e00\u4e9b\u4e2d\u56fd\u516c\u53f8\u63a5\u6d3d\u4ee3\u7406\uff1f\n    Hit-Point\uff1a\u6211\u4eec\u5df2\u7ecf\u6536\u5230\u5efa\u8bae\u5e76\u79ef\u6781\u601d\u8003\uff0c\u4f46\u76ee\u524d\u6211\u4eec\u8fd8\u6ca1\u6709\u8fbe\u5230\u8c08\u8bba\u7ec6\u8282\u7684\u9636\u6bb5\u3002\u4e2d\u6587\u5316\u65b9\u9762\uff0c\u6211\u4eec\u786e\u5b9e\u6709\u5728\u8003\u8651\u5c06\u6e38\u620f\u672c\u5730\u5316\uff0c\u5e76\u4e14\u6211\u4eec\u4f1a\u4e0e\u4ee3\u7406\u5408\u4f5c\u516c\u53f8\u4e00\u8d77\u8ba8\u8bba\u3002\n    \u754c\u9762\uff1a\u76ee\u524d\u300a\u9752\u86d9\u65c5\u884c\u300b\u5728\u4e2d\u56fd\u5730\u533a\u7684\u7528\u6237\u89c4\u6a21\u548c\u8425\u6536\u6bd4\u4f8b\u5982\u4f55\uff1f\u5927\u6982\u6709\u591a\u5c11\u7528\u6237\u4e3a\u6e38\u620f\u4ed8\u8d39\u4e86\uff1f\n    Hit-Point\uff1a\u7531\u4e8e\u73a9\u5bb6\u57fa\u6570\u4ecd\u5728\u589e\u957f\uff0c\u6211\u4eec\u4e0d\u80fd\u7ed9\u51fa\u4e00\u4e2a\u786e\u5207\u6570\u5b57\uff0c\u4e0d\u8fc7\u622a\u81f31\u670826\u65e5\uff0c\u6e38\u620f\u7684\u603b\u4e0b\u8f7d\u91cf\u5df2\u7ecf\u8fbe\u5230\u4e861100\u4e07\u3002\u5728\u6e38\u620f\u5185\u8d2d\u4e0a\u6570\u5b57\u4e5f\u5728\u8fc5\u901f\u53d8\u5316\uff0c\u6211\u4eec\u7edf\u8ba1\u5230\u5728\u65e5\u6d3b\u8dc3\u7528\u6237\u4e2d\uff0c\u67093%-8%\u7684\u73a9\u5bb6\u4f7f\u7528\u4e86\u5185\u8d2d\u8d2d\u4e70\u4e86\u4e09\u53f6\u8349\u3002\u300a\u9752\u86d9\u65c5\u884c\u300b\u662f\u4e00\u6b3e\u57fa\u672c\u4e0a\u53ef\u4ee5\u514d\u8d39\u73a9\u7684\u6e38\u620f\uff0c\u4f46\u4f3c\u4e4e\u6709\u6bd4\u6211\u4eec\u60f3\u8c61\u4e2d\u66f4\u591a\u7684\u7528\u6237\u8fdb\u884c\u4e86\u5185\u8d2d\u3002\n    \u754c\u9762\uff1a\u300a\u9752\u86d9\u65c5\u884c\u300b\u5e94\u8be5\u662fHit-Point\u5728\u4e2d\u56fd\u6700\u6210\u529f\u7684\u4e00\u6b3e\u6e38\u620f\uff0c\u60a8\u5982\u4f55\u770b\u5f85\u8fd9\u79cd\u6210\u529f\u548c\u5728\u610f\u5916\u6d41\u884c\uff1f\u6709\u6ca1\u6709\u60f3\u8fc7\u76f8\u5173\u539f\u56e0\uff1f\n    Hit-Point\uff1a\u6211\u4eec\u4e00\u76f4\u5728\u8bbe\u8ba1\u5f00\u53d1\u80fd\u88ab\u4e16\u754c\u5404\u5730\u4eba\u4eec\u63a5\u53d7\u7684\u6e38\u620f\uff0c\u4f46\u300a\u9752\u86d9\u65c5\u884c\u300b\u5728\u4e2d\u56fd\u5982\u6b64\u53d7\u6b22\u8fce\u8fd8\u662f\u8d85\u8fc7\u4e86\u6211\u4eec\u7684\u9884\u60f3\uff0c\u6211\u4eec\u6ca1\u6709\u5bf9\u6e38\u620f\u8fdb\u884c\u4efb\u4f55\u63a8\u5e7f\u3002\u4eba\u4eec\u559c\u6b22\u8fd9\u6b3e\u6e38\u620f\u53ef\u80fd\u7684\u539f\u56e0\u662f\uff0c\u300a\u9752\u86d9\u65c5\u884c\u300b\u8bbe\u8ba1\u975e\u5e38\u7b80\u5355\uff0c\u6240\u4ee5\u4eba\u4eec\u975e\u5e38\u613f\u610f\u5c1d\u8bd5\u8fd9\u6b3e\u6e38\u620f\u3002\u53e6\u5916\uff0c\u57fa\u4e8e\u793e\u4ea4\u5a92\u4f53\u548c\u4eba\u4eec\u53e3\u5934\u4f20\u64ad\uff0c\u8ba9\u5b83\u4ea7\u751f\u4e86\u4f20\u64ad\u4e0a\u7684\u8fde\u9501\u53cd\u5e94\u3002\n    \u754c\u9762\uff1a\u5728\u4e2d\u56fd\u7684\u5b89\u5353\u5e02\u573a\uff0c\u6709\u4e00\u4e9b\u672a\u7ecf\u8fc7\u6388\u6743\u7684\u975e\u5b98\u65b9\u76d7\u7248\u6e38\u620f\uff0c\u5728iOS\u4e0a\u4e5f\u6709\u4e86\u4e00\u4e9b\u5c71\u5be8\u6284\u88ad\u65c5\u884c\u300a\u9752\u86d9\u65c5\u884c\u300b\u7684\u6e38\u620f\uff0c\u60a8\u5982\u4f55\u770b\u5f85\u8fd9\u70b9\uff0c\u662f\u5426\u4f1a\u8003\u8651\u91c7\u53d6\u7ef4\u6743\u884c\u52a8\uff1f\n    Hit-Point\uff1a\u76d7\u7248\u7684\u5b58\u5728\u662f\u4e00\u4e2a\u4ee4\u6211\u4eec\u96be\u8fc7\u7684\u95ee\u9898\u3002\u5982\u679c\u7528\u6237\u56e0\u6b64\u800c\u906d\u53d7\u4efb\u4f55\u635f\u5931\uff0c\u6211\u4eec\u4f1a\u611f\u5230\u66f4\u52a0\u96be\u8fc7\u3002\u56e0\u6b64\u6211\u4eec\u8ba4\u4e3a\u6709\u5fc5\u8981\u63d0\u4f9b\u4e00\u4e9b\u5bf9\u7b56\uff0c\u4f46\u9996\u5148\uff0c\u6211\u4eec\u5e0c\u671b\u521b\u9020\u4e00\u4e2a\u80fd\u591f\u5411\u7528\u6237\u4f20\u8fbe\u6b63\u786e\u4fe1\u606f\u7684\u73af\u5883\u3002\n    \u754c\u9762\uff1a\u5bf9\u4e8e\u6e38\u620f\u672a\u6765\u7684\u66f4\u65b0\uff0c\u6709\u6ca1\u6709\u76f8\u5173\u8ba1\u5212\u548c\u6253\u7b97\uff0c\u4e0b\u4e00\u6b65\u66f4\u65b0\u7684\u65b9\u5411\u4f1a\u662f\u600e\u6837\u7684\uff1f\u6bd4\u5982\u662f\u5426\u4f1a\u52a0\u5165\u66f4\u591a\u666f\u70b9\uff0c\u5728\u6e38\u620f\u73a9\u6cd5\u4e0a\u66f4\u52a0\u4e30\u5bcc\u4e00\u4e9b\uff1f\n    Hit-Point\uff1a\u5728\u672a\u6765\u7684\u66f4\u65b0\u4e2d\uff0c\u6211\u4eec\u5c06\u9996\u5148\u589e\u52a0\u9752\u86d9\u5c06\u53c2\u89c2\u7684\u5730\u65b9\u7684\u6570\u91cf\uff0c\u8fd9\u6837\u9752\u86d9\u5c31\u53ef\u4ee5\u53d1\u56de\u66f4\u591a\u7167\u7247\u3002\u73a9\u5bb6\u7ed9\u4e86\u6211\u4eec\u8bb8\u591a\u53cd\u9988\uff0c\u4f8b\u5982\u591a\u8bed\u8a00\u652f\u6301\u7b49\uff0c\u6211\u4eec\u4f1a\u5728\u4e0d\u65ad\u66f4\u65b0\u7684\u540c\u65f6\u4e5f\u4e00\u5e76\u5904\u7406\u8fd9\u4e9b\u53cd\u9988\u3002\n    \u754c\u9762\uff1a\u4e4b\u524d\u7684\u4f5c\u54c1\u300a\u732b\u54aa\u540e\u9662\u300b\u5df2\u7ecf\u6709\u4e86\u4e30\u5bcc\u7684\u5468\u8fb9\u884d\u751f\u54c1\u5f00\u53d1\uff0c\u6bd4\u5982\u7535\u5f71\u7b49\uff0c\u300a\u9752\u86d9\u65c5\u884c\u300b\u4f1a\u4e0d\u4f1a\u6709\u76f8\u5173\u8003\u8651\uff1f\n    Hit-Point\uff1a\u5bf9\u4e8e\u884d\u751f\u4ea7\u54c1\uff0c\u5982\u679c\u53ef\u80fd\u7684\u8bdd\uff0c\u6211\u4eec\u4e00\u5b9a\u4f1a\u8fdb\u884c\u5f00\u53d1\u3002\u5f88\u96be\u60f3\u8c61\u300a\u732b\u54aa\u540e\u9662\u300b\u6700\u540e\u62cd\u6210\u4e86\u7535\u5f71\uff0c\u4f46\u662f\u6e38\u620f\u7684\u4e16\u754c\u89c2\u548c\u4eba\u7269\u8bbe\u5b9a\u662f\u53ef\u4ee5\u4ee5\u5404\u79cd\u65b9\u5f0f\u5229\u7528\u7684\uff0c\u6240\u4ee5\u8c01\u4f1a\u60f3\u5230\u672a\u6765\u4f1a\u53d1\u751f\u4ec0\u4e48\u5462\uff1f\"\"\", window_width = 3, weight = 0.8)\n\n    list = t.extract_kw()\n    for word, rank in list :\n        print('[%s %f]' % (word, rank))\n\nWe can see the output :\n\n::\n\n    [\u65c5\u884c\u9752\u86d9 19.167803]\n    [\u5f00\u53d1\u6e38\u620f 14.831004]\n    [\u73a9\u5bb6 4.801199]\n    [\u4e2d\u56fd 3.508624]\n    [\u7528\u6237 3.118792]\n\nSimilarity of Texts\n~~~~~~~~~~~~~~~~~~~\n\nSimHash\n^^^^^^^\n\n**SimHash** [6]_ , proposed by MS Charikar, as a LSH(Locality Sensitive Hash), can be used to detect the similarity of two passages. There is a simple implementation; by the IDF Model we have and the tfidf keywords extraction, we extract those Top-200 feature words and hash them. Summing and normalizing, we get the SimHash fingerprint. All is done, now we just compute the two's Hamming Distance, and the similarity is represented by the distance.\n\nWe can have a try :  \n\n.. code:: python\n\n    from huhu_seg.simhash import SimHash\n\n    t1 = SimHash(\"\"\"\n    SpaceX\u7684\u201c\u730e\u9e70\u91cd\u578b\u201d\u6210\u4e3a\u5730\u8868\u6700\u5f3a\u5927\u7684\u706b\u7bad\u3002\uff08\u56fe\u6e90\uff1aCNN\uff09\n    \u52a9\u63a8\u706b\u7bad\u540c\u65f6\u5782\u76f4\u7740\u9646\u3002\uff08\u56fe\u6e90\uff1a\u7f51\u7edc\uff09\n    \u6d77\u5916\u7f512\u67087\u65e5\u7535 \u88ab\u8a89\u4e3a\u4e16\u754c\u6700\u5f3a\u5927\u73b0\u5f79\u8fd0\u8f7d\u706b\u7bad\u7684\u91cd\u578b\u730e\u9e70\u706b\u7bad\uff0c\u5f53\u5730\u65f6\u95f4\u5468\u4e8c\uff086\u65e5\uff09\u4e0b\u53483\u70b945\u5206\u5de6\u53f3\u5728\u7f8e\u56fd\u4f5b\u7f57\u91cc\u8fbe\u5dde\u80af\u5c3c\u8fea\u822a\u5929\u4e2d\u5fc3\u53d1\u5c04\u6210\u529f\u3002\n    \u636e\u7f8e\u56fd\u6709\u7ebf\u7535\u89c6\u65b0\u95fb\u7f51\uff08CNN\uff09\u62a5\u9053\uff0c\u7ea6300\u4e07\u4eba\u6536\u770b\u4e86SpaceX\u7684\u73b0\u573a\u76f4\u64ad\uff0c\u4e3a\u4e4b\u6b22\u547c\u3002\u4f0a\u9686\u00b7\u9a6c\u65af\u514b\u5468\u4e00\uff085\u65e5\uff09\u5728\u63a5\u53d7\u7f8e\u56fd\u6709\u7ebf\u7535\u89c6\u65b0\u95fb\u7f51\u91c7\u8bbf\u65f6\u8bf4\uff1a\u201c\u6765\u81ea\u4e16\u754c\u5404\u5730\u7684\u4eba\u4eec\u5c06\u4f1a\u770b\u5230\u8fd9\u573a\u4f1f\u5927\u7684\u706b\u7bad\u53d1\u5c04\uff0c\u540c\u65f6\u4e5f\u4f1a\u662f\u4ed6\u4eec\u89c1\u8fc7\u7684\u6700\u68d2\u7684\u70df\u706b\u8868\u6f14\u3002\u201d\n    SpaceX\u7684\u91cd\u578b\u730e\u9e70\u706b\u7bad\u4e0d\u4ec5\u5c06SpaceX\u9996\u5e2d\u6267\u884c\u5b98\u4f0a\u9686\u00b7\u9a6c\u65af\u514b\u5b89\u6392\u7684\u7279\u65af\u62c9\u8dd1\u8f66\u9001\u5165\u4e86\u592a\u7a7a\uff0c\u800c\u4e14\u88ab\u6210\u529f\u56de\u6536\u4e86\u4e24\u679a\u4e00\u7ea7\u52a9\u63a8\u706b\u7bad\u3002\u53d1\u5c04\u6210\u529f\u540e\uff0c\u4f0a\u9686\u00b7\u9a6c\u65af\u514b\u5bf9\u8bb0\u8005\u8bf4\uff1a\u201c\u6211\u8fd8\u5728\u8bd5\u56fe\u6d88\u5316\u8fd9\u4e2a\u6210\u679c\uff0c\u771f\u50cf\u505a\u68a6\u4e00\u6837\u3002\u201dSpaceX\u57282017\u5e74\u5e74\u521d\u8868\u793a\uff0c\u6709\u4e24\u4f4d\u201c\u592a\u7a7a\u6e38\u5ba2\u201d\u5df2\u7ecf\u4e3a\u4e58\u91cd\u578b\u730e\u9e70\u706b\u7bad\u8fdb\u884c\u73af\u6708\u65c5\u884c\u800c\u4ed8\u4e86\u4e00\u5927\u7b14\u5b9a\u91d1\u3002SpaceX\u5f53\u65f6\u8868\u793a\uff0c\u73af\u6708\u4e4b\u65c52018\u5e74\u53ef\u80fd\u4f1a\u5b9e\u73b0\uff0c\u4e0d\u8fc7SpaceX\u540e\u6765\u6ca1\u518d\u7ed9\u51fa\u65b0\u52a8\u5411\u3002\n    \u636e\u7f8e\u56fd\u6709\u7ebf\u7535\u89c6\u65b0\u95fb\u7f51\u62a5\u9053\uff0c\u65e9\u57282017\u5e749\u6708\uff0c\u4f0a\u9686\u00b7\u9a6c\u65af\u514b\u8868\u793a\u8981\u57282020\u5e74\u5728\u706b\u661f\u964d\u843d\u4e24\u8258\u8d27\u8239\uff0c\u5e76\u8ba1\u5212\u5efa\u9020\u201c\u5de8\u578b\u730e\u9e70\u706b\u7bad\u201d\uff08BFR\uff09\uff0c\u5982\u679c\u706b\u7bad\u56de\u6536\u7684\u90e8\u5206\u53ef\u4ee5\u201c\u91cd\u590d\u5229\u7528\u201d\uff0c\u201c\u4eba\u7c7b\u63a2\u7d22\u706b\u661f\u7684\u6210\u672c\u53ef\u4ee5\u5927\u5927\u51cf\u5c11\u201d\u3002\n    \u4ece\u79d1\u5e7b\u5230\u73b0\u5b9e\u5e76\u4e0d\u662f\u4e00\u8e74\u800c\u5c31\u3002\u4ece2008\u5e749\u670828\u65e5\u730e\u9e701\u53f7\u706b\u7bad\u9996\u6b21\u6210\u529f\u53d1\u5c04\uff0c\u5230\u5982\u4eca\u7684\u91cd\u578b\u730e\u9e70\u706b\u7bad\u7684\u6210\u529f\u53d1\u5c04\u80cc\u540e\uff0c\u6709\u7740SpaceX\u4ed8\u51fa\u7684\u5de8\u5927\u52aa\u529b\u3002\u91cd\u578b\u730e\u9e70\u706b\u7bad\u7684\u6210\u529f\u53d1\u5c04\uff0c\u6807\u5fd7\u7740SpaceX\u7528\u5176\u5f00\u521b\u6027\u6280\u672f\u64bc\u52a8\u706b\u7bad\u884c\u4e1a\u7684\u8bbe\u60f3\u5411\u524d\u8fc8\u8fdb\u4e86\u4e00\u6b65\uff0c\u800c\u5982\u4eca\u4eba\u7c7b\u79fb\u6c11\u706b\u661f\u7684\u68a6\u60f3\u4e5f\u524d\u8fdb\u4e86\u4e00\u6b65\u3002\n    \"\"\") \n    t2 = SimHash(\"\"\"\n    \u963f\u6ce2\u7f57\u65f6\u4ee3\u7684\u592a\u7a7a\u5df2\u6597\u8f6c\u661f\u79fb\u3002\n    \u3000\u3000\u7f8e\u56fd\u5f53\u5730\u65f6\u95f42\u67086\u65e5\uff0c\u79d1\u6280\u5927\u4ea8\u4f0a\u9686\u00b7\u9a6c\u65af\u514b\uff08Elon Musk\uff09\u65d7\u4e0bSpaceX\u516c\u53f8\u7684\u65b0\u578b\u706b\u7bad\u201c\u730e\u9e70\u91cd\u578b\u201d\uff08Falcon Heavy\uff09\u5728\u4f5b\u7f57\u91cc\u8fbe\u5dde\u7684\u80af\u5c3c\u8fea\u822a\u5929\u4e2d\u5fc3\u6210\u529f\u5347\u7a7a\u3002\n    \u3000\u3000\u8fd9\u6807\u5fd7\u7740\u4eba\u7c7b\u822a\u7a7a\u53f2\u4e0a\u7684\u4e00\u4e2a\u65b0\u7684\u91cc\u7a0b\u7891\u300250\u5e74\u524d\uff0c\u66fe\u5728\u540c\u6837\u7684\u53d1\u5c04\u5e73\u53f0\u4e0a\uff0c\u571f\u661f5\u53f7\u8fd0\u8f7d\u706b\u7bad\u9996\u6b21\u628a\u4eba\u7c7b\u5e26\u5411\u6708\u7403\uff0c\u5f00\u542f\u4e86\u201c\u963f\u6ce2\u7f57\u201d\u65f6\u4ee3\u3002\n\n    \u3000\u3000\u8fd0\u529b\u582a\u6bd4\u571f\u661f5\u53f7\n    \u3000\u3000\u730e\u9e70\u91cd\u578b\u53d1\u5c04\u6210\u529f\u540e\uff0c\u7f8e\u56fd\u603b\u7edf\u7279\u6717\u666e\u53d1\u8868Twitter\u79f0\uff1a\u201c\u795d\u8d3a\u9a6c\u65af\u514b\uff0c\u795d\u8d3aSpaceX\u3002\u8fd9\u4e00\u80dc\u5229\u548cNASA\u5546\u4e1a\u4ee5\u53ca\u56fd\u9645\u5408\u4f5c\u4f19\u4f34\u4e00\u8d77\uff0c\u7ee7\u7eed\u5c55\u73b0\u7f8e\u56fd\u6700\u597d\u7684\u5929\u624d\u667a\u6167\u3002\u201d\n    \u3000\u3000\u9a6c\u65af\u514b\u4e5f\u5728\u730e\u9e70\u91cd\u578b\u53d1\u5c04\u6210\u529f\u540e\u901a\u8fc7Twitter\u8868\u793a\uff1a\u201c\u4eceSpaceX\u53d1\u5c04\u7684\u63a7\u5236\u753b\u9762\u6765\u770b\uff0c\u6beb\u65e0\u7591\u95ee\u4e00\u8f86\u6c7d\u8f66\u5df2\u7ecf\u8fdb\u5165\u4e86\u5730\u7403\u8f68\u9053\u3002\u201d\n    \u3000\u3000SpaceX\u7684\u7ade\u4e89\u5bf9\u624b\u84dd\u8272\u8d77\u6e90\uff08Blue Origin\uff09\u7684\u521b\u59cb\u4eba\u3001\u4e9a\u9a6c\u900a\u603b\u88c1\u8d1d\u4f50\u65af\u4e5f\u53d1\u8868Twitter\u795d\u8d3a\u9a6c\u65af\u514b\uff0c\u4e0d\u8fc7\u53ea\u6709\u975e\u5e38\u7b80\u5355\u7684\u4e00\u4e2a\u8bcd\uff1a\u201cWoohoo!\u201d\n    \u3000\u3000\u636e\u7f8e\u56fd\u6709\u7ebf\u7535\u89c6\u65b0\u95fb\u7f51CNN\u62a5\u9053\uff0c\u6709\u7ea6300\u4e07\u4eba\u6536\u770b\u4e86SpaceX\u7684\u73b0\u573a\u76f4\u64ad\u3002\u9a6c\u65af\u514b\u5728\u63a5\u53d7CNN\u91c7\u8bbf\u65f6\u8bf4\uff1a\u201c\u6765\u81ea\u4e16\u754c\u5404\u5730\u7684\u4eba\u4eec\u5c06\u4f1a\u770b\u5230\u8fd9\u573a\u4f1f\u5927\u7684\u706b\u7bad\u53d1\u5c04\uff0c\u540c\u65f6\u4e5f\u4f1a\u662f\u4ed6\u4eec\u89c1\u8fc7\u7684\u6700\u68d2\u7684\u70df\u706b\u8868\u6f14\u3002\u201d\n    \u3000\u3000\u81ea\u5927\u5982\u9a6c\u65af\u514b\uff0c\u5728\u65b0\u706b\u7bad\u53d1\u5c04\u524d\u4e5f\u6709\u62c5\u5fe7\u3002\u4ed6\u6b64\u524d\u66fe\u8868\u793a\uff0c\u7814\u53d1\u8be5\u65b0\u706b\u7bad\u9047\u4e0a\u6311\u6218\uff0c\u9996\u6b21\u8bd5\u5c04\u7684\u6210\u529f\u7387\u53ea\u6709\u4e00\u534a\u3002 \u201c\u6211\u8111\u6d77\u66fe\u7ecf\u51fa\u73b0\u8fc7\u4e00\u4e9b\u753b\u9762\uff0c\u6bd4\u5982\u5728\u53d1\u5c04\u5e73\u53f0\u4e0a\u51fa\u73b0\u7206\u70b8\uff0c\u6216\u8005\u6709\u8f6e\u80ce\u8131\u843d\u6eda\u5f00\u3002\u201d \u4ed6\u8bf4\u3002\n    \u3000\u3000\u4f46\u5e78\u8fd0\u7684\u662f\uff0c\u4e0a\u8ff0\u79cd\u79cd\u90fd\u6ca1\u6709\u53d1\u751f\u3002\n    \u3000\u3000\u201c\u8fd9\u5c31\u50cf\u4e00\u90e8\u79d1\u5e7b\u7247\u7ec8\u4e8e\u8d70\u5411\u4e86\u4e8b\u5b9e\u3002\u201d\u524dNASA\u5b98\u5458\u3001\u9ebb\u7701\u7406\u5de5\u5b66\u9662\uff08MIT\uff09\u963f\u6ce2\u7f57\u5b87\u822a\u6559\u6388Dava 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[1] [MMSEG: A Word Identification System for Mandarin Chinese Text Based on Two Variants of the Maximum Matching Algorithm](http://technology.chtsai.org/mmseg/)\n.. [2] [fxsjy/jieba](https://github.com/fxsjy/jieba)\n.. [3] [\u8bcd\u56fe\u7684\u751f\u6210](http://www.hankcs.com/nlp/segment/the-word-graph-is-generated.html). \n.. [4] [News Keyword Extraction for Topic Tracking](http://ieeexplore.ieee.org/document/4624203/) \u00a0\n.. [5] [TextRank: Bringing Order into Texts](https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf)\n.. [6] [Similarity Estimation Techniques from Rounding Algorithms](https://www.cs.princeton.edu/courses/archive/spr04/cos598B/bib/CharikarEstim.pdf)\n\n\n\n",
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