?!DOCTYPE HTML> 百人牛牛安卓版 深圳seo教程,|络seo中文分词提取Ҏ_SEO指南自媒体「权威教E?/title> <meta name="keywords" content="恒丰娱乐下载,恒丰娱乐在线" /> <meta name="description" content="✅✅ag注册【新用户联系客服就送8-126,百万秒出款】旗下多款游ag注册欢迎您" /> <meta http-equiv="Cache-Control" content="no-transform"/> <script type="text/javascript" src="/style/js/config.js"></script> <script type="text/javascript" src="/style/js/jquery.js"></script> <script type="text/javascript" src="/style/js/jquery.cookie.js"></script> <script type="text/javascript" src="/style/js/cmstop-common.js"></script> <script type="text/javascript" src="/style/js/cmstop-imagezoom.js"></script> <script type="text/javascript" src="/style/js/fet.js"></script> <script type="text/javascript" src="/style/js/repos.js"></script> <link rel="stylesheet" type="text/css" href="/style/css/style.css"/> <script type="text/javascript" src="/style/js/common.js"></script> <script type="text/javascript" src="/style/js/post.js"></script> <meta http-equiv="mobile-agent" 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关于|络seo的关键词提取Ҏ分ؓ有监督、半监督和无监督三种Q?/div> <div>  </div> <div> 1.有监督的关键词抽取算?/div> <div>  </div> <div> 它是建关键词抽取法看作是二分类问题Q判断文档中的词或者短语是或者不是关键词。既然是分类问题Q就需要提供已l标注好的训l预料,利用训练语料训练关键词提取模型,Ҏ模型寚w要抽取关键词的文档进行关键词抽取</div> <div>  </div> <div> 2.半监督的关键词提取算?/div> <div>  </div> <div> 只需要少量的训练数据Q利用这些训l数据构建关键词抽取模型Q然后用模型对新的文本q行关键词提取,对于q些关键词进行h工过滤,过滤得到的关键词加入训l集Q重新训l模型?/div> <div>  </div> <div> 3.无监督的Ҏ</div> <div>  </div> <div> 不需要h工标注的语料Q利用某些方法发现文本中比较重要的词作ؓ关键词,q行关键词抽取?/div> <div>  </div> <div> 有监督的|络seo中文分词提取法需要高昂的人工成本Q因此现有的|络seo中文分词提取主要采用适用性较强的无监督关键词抽取。其|络seo中文分词抽取程如下Q?/div> <p> <img class="large" data-loaded="0" data-loadfunc="0" src="/upload/2020/0603/zo2tw1ysirj.jpg" /></p> <div> ?1 无监督网lseo中文分词抽取程?/div> <div>  </div> <div> 无监督关键词抽取法可以分ؓ三大c,Zl计特征的关键词抽取、基于词图模型的关键词抽取和Z主题模型的关键词抽取?/div> <div>  </div> <div> |络seo中文分词提取法</div> <div>  </div> <div> Zl计特征的关键词提取法</div> <div>  </div> <div> Z于统计特征的关键词抽取算法的思想是利用文档中词语的统计信息抽取文档的关键词。通常文本经q预处理得到候选词语的集合Q然后采用特征值量化的方式从候选集合中得到关键词。基于统计特征的关键词抽取方法的关键是采用什么样的特征值量化指标的方式Q目前常用的有三c:</div> <div>  </div> <div> 1、基于词权重的特征量?/div> <div>  </div> <div> Z词权重的特征量化主要包括词性、词频、逆向文档频率、相对词频、词长等?/div> <div>  </div> <div> 2、基于词的文档位|的特征量化</div> <div>  </div> <div> q种特征量化方式是根据文章不同位|的句子Ҏ档的重要性不同的假设来进行的。通常Q文章的前N个词、后N个词、段首、段、标题、引a{位|的词具有代表性,q些词作为关键词可以表达整个的主题?/div> <div>  </div> <div> 3、基于词的关联信息的特征量化</div> <div>  </div> <div> 词的兌信息是指词与词、词与文档的兌E度信息Q包括互信息、hits倹{A献度、依存度、TF-IDF值等?/div> <div>  </div> <div> 下面介绍几种常用的特征值量化指标?/div> <div>  </div> <div> 词性时通过分词、语法分析后得到的结果。现有的关键词中Q绝大多数关键词为名词或者动名词。一般情况下Q名词与其他词性相比更能表达一文章的主要思想。但是,词性作为特征量化的指标Q一般与其他指标l合使用?/div> <div>  </div> <div> 词频表示一个词在文本中出现的频率。一般我们认为,如果一个词在文本中出现的越是频J,那么q个词就有可能作ؓ文章的核心词。词频简单地l计了词在文本中出现的次敎ͼ但是Q只依靠词频所得到的关键词有很大的不确定性,对于长度比较长的文本Q这个方法会有很大的噪音?/div> <div>  </div> <div> 位置信息</div> <div>  </div> <div> 一般情况下Q词出现的位|对于词来说有着很大的h倹{例如,标题、摘要本w就是作者概括出的文章的中心思想Q因此出现在q些地方的词h一定的代表性,更可能成为关键词。但是,因ؓ每个作者的习惯不同Q写作方式不同,关键句子的位|也会有所不同Q所以这也是一U很宽泛的得到关键词的方法,一般情况下不会单独使用?/div> <div>  </div> <div> 互信息是信息Z概念Q是变量之间怺依赖的度量。互信息q不局限于实值随机变量,它更加一般且军_着联合分布 p(X,Y) 和分解的边缘分布的乘U?p(X)p(Y) 的相似程度。互信息的计公式如下:</div> <p> <img class="normal" data-loaded="0" data-loadfunc="0" src="/upload/2020/0603/fizthbv55ol.jpg" width="459px" /></p> <div> 其中Qp(x,y)是X和Y的联合概率分布函敎ͼp(x)和p(y)分别为X和Y的边~概率分布函数?/div> <div>  </div> <div> 当用互信息作ؓ关键词提取的特征量化Ӟ应用文本的正文和标题构造PAT树,然后计算字符串左右的互信息?/div> <div>  </div> <div> 词跨度是指一个词或者短语字文中首次出现和末ơ出C间的距离Q词跨度大说明q个词对文本重要,可以反映文本的主题。一个词的跨度计公式如下:</div> <div>  </div> <p> <img class="normal" data-loaded="0" data-loadfunc="0" src="/upload/2020/0603/wiwp5ot0vs1.jpg" width="319px" /></p> <div> 其中Qd为阻系敎ͼ通常?.85。是指向|页 i 的网集合。是指网j中的链接指向的集合,是指集合中元素的个数?/div> <div>  </div> <div> TextRank在构建图的时候将节点q|成了句子Qƈ点之间的边引入了权|其中权DCZ个句子的怼E度。其计算公式如下Q?/div> <div>  </div> <div> 公式中的为图中节点和的边的权重。其他符号与PageRank公式相同?/div> <div>  </div> <div> TextRank法除了做网lseo中文分词提取Q还seo定制培训:可以做文本摘要提取,效果不错。但是TextRank的计复杂度很高Q应用不qѝ?/div> <div>  </div> <div> |络seo中文分词提取法</div> <div>  </div> <div> Z主题模型的关键词抽取</div> <div>  </div> <div> Z主题关键词提取算法主要利用的是主题模型中关于主题的分布的性质q行关键词提取。算法步骤如下:</div> <div>  </div> <div> 1、获取候选关键词</div> <div>  </div> <div> 从文章中获取候选关键词。即文本分词,也可以再Ҏ词性选取候选关键词?/div> <div>  </div> <div> 2、语料学?/div> <div>  </div> <div> Ҏ大规模预料学习得C题模型?/div> <div>  </div> <div> 3、计文章主题分?/div> <div>  </div> <div> Ҏ得到的隐含主题模型,计算文章的主题分布和候选关键词分布?/div> <div>  </div> <div> 4、排?/div> <div>  </div> <div> 计算文档和候选关键词的主题相似度q排序,选取前n个词作ؓ关键词?/div> <div>  </div> <div> 法的关键在于主题模型的构徏。主题模型是一U文档生成模型,对于一文章,我们的构思思\是先定几个主题Q然后根据主题想好描qC题的词汇Q将词汇按照语法规则l成句子Q段落,最后生成一文章?/div> <div>  </div> <div> 主题模型也是Zq个思想Q它认ؓ文档是一些主题的混合分布Q主题又是词语的概率分布QpLSA模型是W一个根据这个想法构建的模型。同样地Q我们反q来惻I我们扑ֈ了文档的主题Q然后主题中有代表性的词就能表C文档的核心意思,是文档的关键词?/div> <div>  </div> <div> pLSA模型认ؓQ一文档中的每一个词都是通过一定概率选取某个主题Q然后再按照一定的概率从主题中选取得到q个词语Q这个词语的计算公式为:</div> <div>  </div> <div> <img class="normal" data-loaded="0" data-loadfunc="0" src="/upload/2020/0603/1c51axpahta.jpg" width="163px" /></div> <div>  </div> <div> 一些贝叶斯学派的研I者对于pLSA模型q行了改q,他们认ؓQ文章对应主题的概率以及主题对应词语的概率不是一定的Q也服从一定的概率Q于是就有了现阶D常用的主题模型--LDA主题模型?/div> <div>  </div> <div> LDA是D.M.Blei?003q提出的。LDA采用了词袋模型的Ҏ化了问题的复杂性。在LDA模型中,每一文档是一些主题的构成的概率分布,而每一个主题又是很多单词构成的一个概率分布。同Ӟ无论是主题构成的概率分布q是单词构成的概率分布也不是一定的Q这些分布也服从Dirichlet 先验分布?/div> <div>  </div> <div> 文的生成模型可以用如下图模型表C:</div> <div>  </div> <div> 其中和ؓ先验分布的超参数QؓWk个主题下的所有单词的分布Qؓ文档的主题分布,w为文档的词,z为w所对应的主题?/div> <div>  </div> <div> <img class="large" data-loaded="0" data-loadfunc="0" src="/upload/2020/0603/rgh05css1bl.jpg" /></div> <div>  </div> <div> ?3 Blei在论文中的图模型</div> <div>  </div> <div> DA挖掘了文本的深层语义x本的主题Q用文本的主题来表示文本的也从一定程度上降低了文本向量的l度Q很多h用这U方式对文本做分c,取得了不错的效果。具体LDA的算法在请参考《一文详解LDA主题模型》?/div> <div>  </div> <div> LDA关键词提取算法利用文档的隐含语义信息来提取关键词Q但是主题模型提取的关键词比较宽泛,不能很好的反应文档主题。另外,对于LDA模型的时间复杂度较高Q需要大量的实践训练?/div> <div>  </div> <div> <img class="normal" data-loaded="0" data-loadfunc="0" src="/upload/2020/0603/0lxgdtujuv5.jpg" width="491px" /></div> <div>  </div> <div> |络seo中文分词提取法</div> <div>  </div> <div> 应用</div> <div>  </div> <div> 现阶D,文本的关键词提取在基于文本的搜烦、推荐以及数据挖掘领域有着很广泛的应用。同时在实际应用中,因ؓ应用环境的复杂性,对于不同cd的文本,例如长文本和短文本,用同一U网lseo中文分词提取Ҏ得到的效果ƈ相同。因此,在实际应用中针对不同的条件环境所采用的算法会有所不同Q没有某一cȝ法在所有的环境下都有很好的效果?/div> <div>  </div> <div> 相对于上文中所提到的算法,一些组合算法在工程上被大量应用以I补单法的不I例如TF-IDF法与TextRank法相结合,或者综合TF-IDF与词性得到关键词{。同Ӟ工程上对于文本的预处理以及文本分词的准确性也有很大的依赖。对于文本的错别字,变Ş词等信息Q需要在预处理阶D予以解冻I分词法的选择Q未d词以及歧义词的识别在一定程度上对于关键词突提取会又很大的媄响?/div> <div>  </div> <div> 关键词提取是一个看似简单,在实际应用中却十分棘手的dQ从现有的算法的基础上进行工E优化,达观数据在这斚w做了很大的努力ƈ且取得了不错的效果?/div> <div>  </div> <div> <img class="normal" data-loaded="0" data-loadfunc="0" src="/upload/2020/0603/ttchg2co1z0.jpg" width="462px" /></div> <div>  </div> <div> |络seo中文分词提取法</div> <div>  </div> <div> ȝ</div> <div>  </div> <div> 本文介绍了三U常用的无监督的关键词提取算法,q介l了其优~点。关键词提取在文本挖掘领域具有很qK的应用,现有的方法也存在一定的问题Q我们依然会在关键词提取的问题上l箋努力研究Q也Ƣ迎大家U极交流?/div> </div> <div id="hhskapdz64" class="mood"> <div id="cyEmoji" role="cylabs" data-use="emoji"></div> </div> <div id="hhskapdz64" class="avow"> <div id="hhskapdz64" class="avowtitle"></div> 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