关键词代码-生成器
php实现自动获取生成关键词功能
php实现自动获取生成关键词功能以前写程序一直在逃避这个问题，tag什么的都是要求使用程序的人自行输入，对于某些懒人及为了程序的体验，则是希望可以有自动生成文章关键词，自动获取文章tag的类似功能，这次为了迎接新的项目，所以捣鼓了一晚上，研究了一下这个功能。
要实现自动获取关键词的功能，大概可以分成三步1，通过分词算法将标题和内容分别进行分割，提取出关键词和频度。
当前主要的两个算法是中科院的ICTCLAS和隐马尔可夫模型。
但这两个都太高端，有一定的门槛，且都是只支持C++/JAVA。
基于PHP的当前有两个是值得推荐的PSCWS和HTTPCWS。
SCWS于2008-03-08发布1.0.0 正式版，到现在最新版本已经到了1.0.4。
PSCWS是它的PHP版。
而HTTPCWS是张宴开发的，之前叫PHPCWS。
PHPCWS 先使用“ICTCLAS 3.0 共享版中文分词算法”的API 进行初次分词处理，再使用自行编写的“逆向最大匹配算法”对分词和进行词语合并处理，并增加标点符号过滤功能，得出分词结果。
不过很遗憾目前仅支持Linux系统，尚未移植到win平台上。
2，将提取结果与现有词库进行比较，处理，去除无用的词得到最符合规则的关键词。
这里主要就是要看词库了，我们可以自己定义词库，也可以使用现有的成熟词库。
比如新浪和网易博客都有这个功能,。
他们分词应该有不错的词库，因为他们都是大网站，而我呢，区区一个小程序员，不可能搞到什么权威的词库，所以只能从现有的开源程序上入手，看看他们的词库。
3，在处理后的提取结果中选择适当的作为最终的关键词，得到最符合当前内容的关键词，在这个阶段就是具体情况具体分析了，无论如何也不可能达到人的那种智能化。
最多是。
当前PHP类CMS都自有自己的提取关键词系统。
目前在网络上流传最广的是DEDECMS的分词源码，我做了测试，发现相当的呆，效果很不好。
它先设置一个关键词长度，确定获取关键词的数量，然后取词，它认为标题分好的词就是所需关键词，在加上从正文中读取关键词只到达到这个所设置的长度，就是最终关键词了。
flac 3d 网格生成器概述
FLAC 3D学习笔记之网格生成器概述（一）FLAC生成网格一、网格生成器概述1. Generate命令可以调出网格生成器。
1) Generate zone：生成基本形状的网格。
gen zone可以调用的基本形状的网格关键词keyword 简写含义方向brick b 块状型(Brick-shaped mesh)； 3dbrick db 变形块状型(Degenerate Brick mesh) 3wedge w 楔型(Wedge-shaped mesh 3pyramid p 角锥型(Pyramid-shaped mesh) 3tetra tet 四面体(Tetrahedron-shaped mesh) 3cylinder c 圆柱型(Cylinder-shaped mesh) 3redbrick redb 放射状块状型(Radial graded mesh around Brick) 4radtunnel radt 放射状隧道型(Radial graded mesh around parallelepiped-shaped Tunnel) 4 radcylinder radc 放射状圆柱型(Radial graded mesh around Cylindrical-shaped tunnel) 4 cshell cs 圆柱型壳状物(Cylindrical Shell mesh) 4cylint cylint 圆柱交叉型(Cylinder Intersection) 5tunint t 隧道交叉型(Tunnel Intersection) 5Gen zon 生成基本形状网格的常用关键词关键词keyword 含义关键词keyword 含义dimension(dim) 内部区域的尺寸 p0-p16 各种形状网格的参考点edge 网格的边长 ratio(rat) 单元尺寸大小的比率fill 用网格填充的内部区域 size 网格在每个方向上的单元数目gen zon命令如下：generate zone <keyword> &p0 ( x0,y0,z0 ) p1 ( x1,y1,z1 ) p2 ( x2,y2,z2 ) p3 ( x3,y3,z3 ) &p4 ( x4,y4,z4 ) p5 ( x5,y5,z5 ) p6 ( x6,y6,z6 ) p7 ( x7,y7,z7 ) &p8 ( x8,y8,z8 ) p9 ( x9,y9,z9 ) p10 ( x10,y10,z10 ) p11 ( x11,y11,z11 ) &p12 ( x12,y12,z12 ) p13 ( x13,y13,z13 ) p14 ( x14,y14,z14 ) &p15 ( x15,y15,z15 ) p16 ( x16,y16,z16 )size n1<n2><n3><n4><n5> &ratio r1<r2><r3><r4><r5> &<keyword>其中表示为网格的型式;p0~p16为参考点,依不同型式之网格有不同数量之参考点,其中( xi,yi,zi )为各个参考点之座标; n1 <n2><n3><n4><n5>表对网格切分几个元素并针对不同区域切分,切分区域主要依不同型式之网格而定; r1<r2><r3><r4><r5>表对网格切分之元素依比例缩放。
如何写速卖通标题(2) 关键词及标题公式
一、关键词整理关键词来源分3块：选词专家、搜索联想词、直通车关键词。
1、选词专家①进入速卖通后台--生意参谋--市场--选词专家页面。
②打开下载下来Excel，把搜索人气一列，转换成数字，然后降序排列。
③对翻译词一列去除重复项。
④创建一个《标题关键词模板.xlsx》Excel，把上面的关键词整理到这个Excel里，后续上新的时候，就可以直接使用这个表格。
这个表格里的关键词每2-3周更新一次。
2、搜索联想词搜索联想词是指在速卖通搜索框中输入一个关键词，下面会自动关联出相关的热词。
可以把这些词也整理到上面的关键词表格Excel里，不过这类词在这里没法看到搜索指数，可以按照关键词展示顺序整理。
3、直通车关键词直通车关键词可以根据搜索热度、市场均价查看某个关键词的热度，这些关键词也可以在标题中使用。
你可以把这些关键词也整理到上面的关键词列表Excel中。
上面三种关键词来源，整理出来的《标题关键词模板.xlsx》Excel是非常重要的，保持经常更新，所有的运营人员都可以按照这个表格来拼接标题。
以上手动整理关键词比较麻烦，大家可以尝试《AE标题生成器&关键词》工具，可以自动整理关键词，并可以批量生成标题。
官网地址：二、速卖通标题的公式标题公式=【产品词+属性词+同义词+类目词+流量词】+【产品词+属性词+同义词+类目词+流量词】+【……】+ 万能词(前面的词实在凑不够，就用这类词凑) 说明：【……】表示前面【】里的内容可以循环使用。
比如，长裤，∙所属类目：服装/服饰配件(Apparel & Accessories) > 女装(Women's Clothing) > 长裤/九分七分五分裤(Pants & Capris)∙产品词：Trousers∙属性词（材质/特点/颜色/风格等）: ➢White-白色, ➢pure cotton-纯棉, ➢high waist-高腰, ➢summer-夏季, ➢Ankle-Length-长度到脚踝, ➢European style-欧美风（或Western style）∙同义词：Pants、Bottoms、Long Trousers∙类目词：Women's Clothing/women clothing/female clothing/Girl Clothing/Apparel ∙万能词：Hot Sale/2020 New/Brand/Wholesale price/Professional当然，标题公式也不止这一种，我们整体测试下来，这一个公式效果很不错，所有我们后续整理关键词，也是按照这样的标准来分类。
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java生成汉语词汇
java生成汉语词汇全文共四篇示例，供读者参考第一篇示例：我们需要明确生成汉语词汇的规则。
汉语词汇通常由汉字组成，而一个汉字通常由一个或多个字母组成。
所以我们可以通过随机选择字母来生成随机的汉语词汇。
除了选择字母，我们还可以考虑加上汉字的声调和音节结构，使得生成的词汇更加真实。
我们需要考虑如何在Java中实现汉语词汇的生成。
Java提供了丰富的字符串处理类和方法，可以方便地操作和处理字符串。
我们可以通过Java的随机数生成器来生成随机的汉字，然后将这些汉字组合成汉语词汇。
我们还可以通过Java的正则表达式来验证和规范生成的汉语词汇。
下面是一个简单的Java代码示例，用于生成随机的汉语词汇：```javaimport java.util.Random;public class ChineseWordGenerator {// 汉字的Unicode编码范围private static final int START = 0x4e00;private static final int END = 0x9fa5;Random random = new Random();for (int i = 0; i < length; i++) {int code = random.nextInt(END - START + 1) + START;sb.append((char) code);}return sb.toString();}第二篇示例：在计算机编程领域中，生成汉语词汇是一项非常具有挑战性和实用性的任务。
在很多实际应用中，我们需要大量的汉语词汇来进行文本处理、自然语言处理等操作。
而手动编写这些词汇是非常耗时且容易出错的。
利用计算机程序来生成大量的汉语词汇是一个非常有意义的任务。
在计算机编程语言中，Java是一种非常常用的语言，具有广泛的应用领域和丰富的库函数。
下面我们将介绍如何利用Java生成汉语词汇。
textrank算法java实现
textrank算法java实现TextRank算法是一种基于图的无监督关键词提取和文本摘要算法，可以用于自动抽取文本中的关键词和生成文本摘要。
本文将介绍如何使用Java实现TextRank算法，并给出一个简单的示例。
我们需要了解TextRank算法的原理。
TextRank算法基于图的排序算法PageRank，在PageRank算法的基础上进行了改进。
它将文本中的句子或单词作为节点，使用共现关系构建图，并利用节点之间的关系进行排序。
具体步骤如下：1. 对文本进行分词，将句子或单词作为节点。
2. 根据共现关系构建图，使用窗口大小为k的滑动窗口，统计窗口内的共现次数。
3. 根据共现次数构建邻接矩阵，矩阵中的每个元素表示两个节点之间的边的权重。
4. 根据邻接矩阵计算节点之间的关系得分，即PageRank值。
5. 根据节点的PageRank值进行排序，得到关键词或摘要。
接下来，我们将使用Java实现TextRank算法。
首先，我们需要使用一个开源的分词工具，例如jieba分词库。
然后，我们可以按照以下步骤进行实现：1. 导入分词库和其他必要的依赖。
2. 加载待处理的文本数据。
3. 使用分词库对文本进行分词。
4. 构建图，统计共现关系。
5. 根据共现关系构建邻接矩阵。
6. 根据邻接矩阵计算节点之间的关系得分，即PageRank值。
7. 根据节点的PageRank值进行排序，得到关键词或摘要。
下面是一个简单的示例代码：```javaimport com.huaban.analysis.jieba.JiebaSegmenter;import java.util.*;public class TextRank {public static void main(String[] args) {// 加载待处理的文本数据String text = "这是一段待处理的文本数据。
";// 使用jieba分词库对文本进行分词JiebaSegmenter segmenter = new JiebaSegmenter();List<String> words = segmenter.sentenceProcess(text);// 构建图，统计共现关系Map<String, Map<String, Integer>> coOccurrences = new HashMap<>();int windowSize = 5;for (int i = 0; i < words.size(); i++) {String word = words.get(i);Map<String, Integer> coOccurrence = coOccurrences.getOrDefault(word, new HashMap<>());for (int j = i - windowSize; j <= i + windowSize; j++) { if (j >= 0 && j < words.size() && j != i) {String coWord = words.get(j);coOccurrence.put(coWord,coOccurrence.getOrDefault(coWord, 0) + 1);}}coOccurrences.put(word, coOccurrence);}// 根据共现关系构建邻接矩阵Map<String, Map<String, Double>> adjacencyMatrix = new HashMap<>();for (Map.Entry<String, Map<String, Integer>> entry : coOccurrences.entrySet()) {String word = entry.getKey();Map<String, Integer> coOccurrence =entry.getValue();double sum = coOccurrence.values().stream().mapT oInt(Integer::intValue).su m();Map<String, Double> adjacency = new HashMap<>(); for (Map.Entry<String, Integer> coEntry : coOccurrence.entrySet()) {String coWord = coEntry.getKey();int count = coEntry.getValue();double weight = count / sum;adjacency.put(coWord, weight);}adjacencyMatrix.put(word, adjacency);}// 根据邻接矩阵计算PageRank值Map<String, Double> pageRank = new HashMap<>();double dampingFactor = 0.85;double convergenceThreshold = 0.0001;double initialScore = 1.0 / words.size();for (String word : words) {pageRank.put(word, initialScore);}boolean converged = false;while (!converged) {Map<String, Double> newPageRank = new HashMap<>();for (Map.Entry<String, Map<String, Double>> entry : adjacencyMatrix.entrySet()) {String word = entry.getKey();Map<String, Double> adjacency = entry.getValue(); double score = 0.0;for (Map.Entry<String, Double> coEntry : adjacency.entrySet()) {String coWord = coEntry.getKey();double weight = coEntry.getValue();score += pageRank.get(coWord) * weight;}newPageRank.put(word, (1 - dampingFactor) + dampingFactor * score);}converged = true;for (Map.Entry<String, Double> entry : pageRank.entrySet()) {String word = entry.getKey();double oldScore = entry.getValue();double newScore = newPageRank.getOrDefault(word, 0.0);if (Math.abs(newScore - oldScore) > convergenceThreshold) {converged = false;break;}}pageRank = newPageRank;}// 根据PageRank值进行排序List<Map.Entry<String, Double>> sortedPageRank = new ArrayList<>(pageRank.entrySet());sortedPageRank.sort(paringByValue(Comparat or.reverseOrder()));// 输出关键词或摘要int topK = 5;for (int i = 0; i < Math.min(topK, sortedPageRank.size());i++) {Map.Entry<String, Double> entry = sortedPageRank.get(i);String word = entry.getKey();double score = entry.getValue();System.out.println(word + "：" + score);}}}```通过以上代码，我们可以实现对文本进行关键词提取和摘要生成。
使用ChatGPT技术实现关键词提取的方法与技巧
使用ChatGPT技术实现关键词提取的方法与技巧ChatGPT是一种基于人工智能技术的自然语言处理模型，它通过大规模的预训练和微调，在文本生成和理解方面取得了令人瞩目的效果。
关键词提取作为一种重要的文本挖掘技术，在信息检索、文本分类和文本摘要等领域有着广泛的应用。
本文将介绍使用ChatGPT技术实现关键词提取的方法与技巧。
一、ChatGPT简介ChatGPT是由OpenAI开发的一种语言模型，它通过使用大量的语料库进行训练，可以生成和理解自然语言。
ChatGPT可以被应用于很多领域，例如对话系统、自动摘要和智能客服等。
关键词提取是ChatGPT的另一个重要应用领域，下面将介绍使用ChatGPT实现关键词提取的方法。
二、ChatGPT关键词提取方法使用ChatGPT进行关键词提取有两种常用的方法：基于生成和基于分类。
1. 基于生成的关键词提取方法基于生成的关键词提取方法是指通过ChatGPT生成与文本相关的关键词。
该方法的基本思想是通过改变生成文本的条件，使得生成的文本具有相关的关键词。
具体的步骤如下：(1) 输入文本：首先，将待提取关键词的文本作为ChatGPT的输入。
例如，我们可以输入一段新闻报道的文本作为输入。
(2) 生成文本：接下来，通过ChatGPT生成与输入文本相关的文本。
生成的文本可以是包含关键词的句子或段落。
(3) 提取关键词：最后，通过自然语言处理技术，从生成的文本中提取关键词。
常用的技术包括词频统计、TF-IDF算法和TextRank算法等。
2. 基于分类的关键词提取方法基于分类的关键词提取方法是指通过ChatGPT进行文本分类，将关键词提取看作是一个分类问题。
该方法的基本思想是通过对文本进行分类，判断每个词语是否为关键词。
具体的步骤如下：(1) 数据准备：首先，需要准备训练数据。
将一些已经标注好的文本作为样本，将每个词语作为特征，标注是否为关键词作为标签。
(2) 模型训练：接下来，使用ChatGPT进行文本分类模型的训练。
20个在线代码编辑器和开发工具
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