基于综合权重的多文档关键词抽取算法

多文档关键词抽取是进行在多篇文献中找出最能反映整体主题的关键词。对几种关键词抽取算法进行了介绍,分析了各自的优缺点,在TF/PDF算法的基础上,采用文献内和文献间综合权重的方法,提出了一种基于综合权重的多     

基于长尾关键词的SEO策略

长尾关键词是长尾理论在搜索引擎优化上的一个延伸。通过对长尾理论和SEO的研究, 结合长尾关键词的研究现状, 提出一个科学系统的基于长尾关键词的SEO策略。该策略有助于快速提高网站众多长尾词的排名并提升网站流量, 适用于各个领域的电子商务网站。     

基于HMM的应用层DoS攻击检测方法

为了能快速有效地识别出应用层DoS攻击, 提出一种基于HMM的应用层DoS攻击检测方法。该方法以应用层协议关键词和关键词之间的时间间隔作为输入, 采用隐马尔可夫模型来快速检测应用层DoS攻击。实验结果表明, 该方法对应用层上的多种DoS攻击都具有很高的检测率和较低的误报率。     

融合图结构与节点关联的关键词提取方法

单篇文本的关键词提取可应用于网页检索、知识理解与文本分类等众多领域。该文提出一种融合图结构与节点关联的关键词提取方法,能够在脱离外部语料库的情况下发现单篇文本的关键词。首先,挖掘文本的频繁封闭项集并生成强关联规则集合;其次,取出强关联规则集合中的规则头与规则体作为节点,节点之间有边当且仅当彼此之间存在强关联规则时,边权重定义为关联规则的关联度,将强关联规则集合建模成关联图;再次,综合考虑节点的图结构属性、语义信息和彼此的关联性,设计一种新的随机游走算法计算节点的重要性分数;最后,为了避免抽取的词项之间有语义包含关系,对节点进行语义聚类并选取每个类的类中心作为关键词提取结果。通过设计关联图模型参数的选取、关键词的提取规模、不同算法对比3个实验,在具有代表性的中英文数据上证明了该方法能够有效提升关键词提取的效果。     

一种杂交特征选择算法及其应用

提出一种新的特征选择算法,融合了信息熵和关联规则理论,并应用于检测P2P下载的数据集,目的是检测局域网内占用较大带宽下载的用户。该方法计算含有和不含有P2P下载的数据集各属性的熵值,对两者进行对比,将熵值变化大的属性标记为感兴趣属性。利用关联规则对含有P2P下载的数据集挖掘出大于一定阈值的1频繁项目集的属性,得到精简的属性集。利用该精简属性集,提出一种检测模型,用于检测局域网中P2P大规模下载的用户,取得了较好的效果。     

一种基于领域本体的混合信息检索模型

针对语义检索中本体不能提供所有知识的问题,提出一种基于领域本体的混合信息检索模型。该模型利用领域本体中概念间的语义关系,结合关键词检索和语义检索,建立关键词基础矩阵和语义扩展矩阵两层索引矩阵,使系统检索在没有可用本体知识时能自动调整为关键词检索,保证一定的检索性能。两者的结合有效改善了检索性能。     

以关键词抽取为核心的文摘句选择策略

针对面向查询的多文档自动文摘,该文提出了一种以关键词抽取为核心的文摘句选择策略。通过查询扩展的相关技术得到相关多文档集中词语的查询相关性特征,利用最大似然估计法得到语料中词语的话题相关性特征,并将这两个特征值进行特征融合得到词语的重要度以确定关键词。然后通过关键词的重要度来给候选句打分,进一步利用改进的MMR(Maximal Marginal Relevance)技术来调整候选句的得分,最后生成文摘。该文将特征融合引入到词语层面,在DUC2005的语料中测试取得了较好的效果。     

一种基于遗传算法的查询关键词形成技术

分析针对离散的关系数据构造有效查询关键词的关键因素,并在此基础上提出一种基于遗传算法的查询关键词形成方法GQFA（ Genetic-based Query Forming Algorithm ）。对于目标属性,在训练集上应用遗传算法,学习得到与目标属性强相关的属性子集,进而形成合适的查询关键词。实验结果表明,该方法形成的查询关键词能够较好地满足应用需求。     

A comprehensive keyword analysis of online privacy policies

ABSTRACT

Online privacy policies are known to have inconsistent formats and incomplete content. They are also hard to understand and do not effectively help individuals to make decisions about the data practices of the online service providers. Several studies have focused on the deficiencies of privacy policies such as length and readability. However, a very limited number of studies have explored the content of privacy policies. This paper aims to shed some lights on the content of these legal documents. To this end, we performed a comprehensive analysis of keywords and content of over 2000 online policies. Policies were collected from variety of websites, application domains, and regulatory regimes. Topic modeling algorithms, such as Latent Dirichlet Allocation, were used for topic coverage analysis. This study also measured the coverage of ambiguous words in privacy policies. Lastly, a method was used to evaluate keyword similarity between privacy policies which belonged to different regulatory framework or applications. The findings suggested that regulations have an impact on the selection of terminologies used in the privacy policies. The results also suggested that European policies use fewer ambiguous words but use more words such as cookie and compliance with the regional regulations. We also observed that the seed keywords extracted for each section of privacy policies were consistently used in all policies regardless of the application domain and regulations.     

XDist： an effective XML keyword search system with re-ranking model based on keyword distribution

Keyword search enables web users to easily access XML data without understanding the complex data schemas. However, the native ambiguity of keyword search makes it arduous to select qualified relevant results matching keywords. To solve this problem, researchers have made much effort on establishing ranking models distinguishing relevant and irrelevant passages, such as the highly cited TF＊IDF and BM25. However, these statistic based ranking methods mostly consider term frequency, inverse document frequency and length as ranking factors, ignoring the distribution and connection information between different keywords. Hence, these widely used ranking methods are powerless on recognizing irrelevant results when they are with high term frequency, indicating a performance limitation. In this paper, a new searching system XDist is accordingly proposed to attack the problems aforementioned. In XDist, we firstly use the semantic query model maximal lowest common ancestor （MAXLCA） to recognize the returned results of a given query, and then these candidate results are ranked by BM25. Especially, XDist re-ranks the top several results by a combined distribution measurement （CDM） which considers four measure criterions： term proximity, intersection of keyword classes, degree of integration among keywords and quantity variance of keywords. The weights of the four measures in CDM are trained by a listwise learning to optimize method. The experimental results on the evaluation platform of INEX show that the re-ranking method CDM can effectively improve the performance of the baseline BM25 by 22% under iP[0.01] and 18% under MAiP. Also the semantic model MAXLCA and the search engine XDist perform the best in their respective related fields.