基于LDA模型的新闻话题分类研究

针对部分网站中新闻话题没有分类或者分类不清等问题,将LDA模型应用到新闻话题的分类中。首先对新闻数据集进行LDA主题建模,根据贝叶斯标准方法选择最佳主题数,采用Gibbs抽样间接计算出模型参数,得到数据集的主题概率分布;然后根据JS距离计算文档之间的语义相似度,得到相似度矩阵;最后利用增量文本聚类算法对新闻文档聚类,将新闻话题分成若干个不同结构的子话题。实验结果显示表明该方法能有效地实现对新闻话题的划分。     

Incorporating self-organizing map with text mining techniques for text hierarchy generation

Self-organizing maps (SOM) have been applied on numerous data clustering and visualization tasks and received much attention on their success. One major shortage of classical SOM learning algorithm is the necessity of predefined map topology. Furthermore, hierarchical relationships among data are also difficult to be found. Several approaches have been devised to conquer these deficiencies. In this work, we propose a novel SOM learning algorithm which incorporates several text mining techniques in expanding the map both laterally and hierarchically. On training a set of text documents, the proposed algorithm will first cluster them using classical SOM algorithm. We then identify the topics of each cluster. These topics are then used to evaluate the criteria on expanding the map. The major characteristic of the proposed approach is to combine the learning process with text mining process and makes it suitable for automatic organization of text documents. We applied the algorithm on the Reuters-21578 dataset in text clustering and categorization tasks. Our method outperforms two comparing models in hierarchy quality according to users’ evaluation. It also receives better F1-scores than two other models in text categorization task.     

一种基于概念聚类的中文文本类簇主题提取方法

本文引入HowNet知识库,实现中文文档的概念聚类．提高文本聚类分析的效率;应用形式概念分析的技术对概念聚类后的中文文本类簇的主题进行抽取。并对类簇间关联进行分析,提高了文本聚类结果的可读性。最后,通过两个实验,评测了该聚类分析和类簇主题抽取方法的优缺点。     

一种基于主题的概率文档相关模型

现有文档关系分析模型难以从主题层次上判别文档相关性.为此,提出了一个基于主题的概率文档相关模型(TPDC).TPDC借助Latent Dirichlet Allocation模型学习文档的主题结构;在计算出主题后验概率和主题相似度的基础上推导出文档后验概率;基于文档后验概率构建文档相关性分析模型.实验结果证明,TPDC模型在文档检索精度和文档压缩程度两方面优于向量空间模型,因而更能胜任实际应用中的文档检索任务.     

一种基于SA_LDA模型的文本相似度计算方法

计算文本的相似度是许多文本信息处理技术的基础。然而,常用的基于向量空间模型(VSM)的相似度计算方法存在着高维稀疏和语义敏感度较差等问题,因此相似度计算的效果 并不理想。在传统的LDA(Latent Dirichlet Allocation)模型的基础上,针对其需要人工确定主题数目的问题,提出了一种能通过模型自身迭代确定主题个数的自适应LDA(SA_LDA)模型。然后,将其引入文本的相似度计算中,在一定程度上解决了高维稀疏等问题。通过实验表明,该方法能自动确定模型主题的个数,并且利用该模型计算文本相似度时取得了比VSM模型更高的准确度。     

一种基于概念聚类的中文文本类簇主题提取方法

本文引入HowNet知识库,实现中文文档的概念聚类,提高文本聚类分析的效率;应用形式概念分析的技术对概念聚类后的中文文本类簇的主题进行抽取,并对类簇间关联进行分析,提高了文本聚类结果的可读性。最后,通过两个实验,评测了该聚类分析和类簇主题抽取方法的优缺点。     

文本数据主题挖掘与关联搜索研究

文本数据是存储和交换信息最自然的方式,文本挖掘技术可以发现海量文本数据中隐藏的潜在知识模式。研究了文本数据主题挖掘与关联搜索技术,首先通过文本解析提取、分词预处理和索引等进行文本信息处理,然后利用基于潜在语义关系的主题发现模型挖掘大量文本数据中隐藏的主题信息,最后利用主题模型计算关键词间的关联程度进行查询扩展,从而实现关联搜索。实现了一个文本数据挖掘与关联搜索的原型系统,对Tancorp数据集进行主题发现和关联搜索,并以视化和网页同步显示关联搜索的过程。     

Text document clustering based on neighbors

Clustering is a very powerful data mining technique for topic discovery from text documents. The partitional clustering algorithms, such as the family of k-means, are reported performing well on document clustering. They treat the clustering problem as an optimization process of grouping documents into k clusters so that a particular criterion function is minimized or maximized. Usually, the cosine function is used to measure the similarity between two documents in the criterion function, but it may not work well when the clusters are not well separated. To solve this problem, we applied the concepts of neighbors and link, introduced in [S. Guha, R. Rastogi, K. Shim, ROCK: a robust clustering algorithm for categorical attributes, Information Systems 25 (5) (2000) 345–366], to document clustering. If two documents are similar enough, they are considered as neighbors of each other. And the link between two documents represents the number of their common neighbors. Instead of just considering the pairwise similarity, the neighbors and link involve the global information into the measurement of the closeness of two documents. In this paper, we propose to use the neighbors and link for the family of k-means algorithms in three aspects: a new method to select initial cluster centroids based on the ranks of candidate documents; a new similarity measure which uses a combination of the cosine and link functions; and a new heuristic function for selecting a cluster to split based on the neighbors of the cluster centroids. Our experimental results on real-life data sets demonstrated that our proposed methods can significantly improve the performance of document clustering in terms of accuracy without increasing the execution time much.     

Followee recommendation based on text analysis of micro-blogging activity

Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity offered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower receives all the micro-blogs from his/her followees. Recent research efforts on understanding micro-blogging as a novel form of communication and news spreading medium have identified three different categories of users in these systems: information sources, information seekers and friends. As social networks grow in the number of registered users, finding relevant and reliable users to receive interesting information becomes essential. In this paper we propose a followee recommender system based on both the analysis of the content of micro-blogs to detect users' interests and in the exploration of the topology of the network to find candidate users for recommendation. Experimental evaluation was conducted in order to determine the impact of different profiling strategies based on the text analysis of micro-blogs as well as several factors that allows the identification of users acting as good information sources. We found that user-generated content available in the network is a rich source of information for profiling users and finding like-minded people.     

A spectral analysis approach to document summarization: Clustering and ranking sentences simultaneously

Automatic document summarization aims to create a compressed summary that preserves the main content of the original documents. It is a well-recognized fact that a document set often covers a number of topic themes with each theme represented by a cluster of highly related sentences. More important, topic themes are not equally important. The sentences in an important theme cluster are generally deemed more salient than the sentences in a trivial theme cluster. Existing clustering-based summarization approaches integrate clustering and ranking in sequence, which unavoidably ignore the interaction between them. In this paper, we propose a novel approach developed based on the spectral analysis to simultaneously clustering and ranking of sentences. Experimental results on the DUC generic summarization datasets demonstrate the improvement of the proposed approach over the other existing clustering-based approaches.     

基于本体实现对网页文本的自动主题分类

提出了一种实现对中文网页进行自动分类的平衡差值法,它利用本体中主题概念的层次结构和主题词、特征项的各种语义关系,降低了分类算法的复杂性和计算量。试验表明,该方法可以获得85％以上的网页分类准确率。     

Temporal contexts: Effective text classification in evolving document collections

The management of a huge and growing amount of information available nowadays makes Automatic Document Classification (ADC), besides crucial, a very challenging task. Furthermore, the dynamics inherent to classification problems, mainly on the Web, make this task even more challenging. Despite this fact, the actual impact of such temporal evolution on ADC is still poorly understood in the literature. In this context, this work concerns to evaluate, characterize and exploit the temporal evolution to improve ADC techniques. As first contribution we highlight the proposal of a pragmatical methodology for evaluating the temporal evolution in ADC domains. Through this methodology, we can identify measurable factors associated to ADC models degradation over time. Going a step further, based on such analyzes, we propose effective and efficient strategies to make current techniques more robust to natural shifts over time. We present a strategy, named temporal context selection, for selecting portions of the training set that minimize those factors. Our second contribution consists of proposing a general algorithm, called Chronos, for determining such contexts. By instantiating Chronos, we are able to reduce uncertainty and improve the overall classification accuracy. Empirical evaluations of heuristic instantiations of the algorithm, named WindowsChronos and FilterChronos, on two real document collections demonstrate the usefulness of our proposal. Comparing them against state-of-the-art ADC algorithms shows that selecting temporal contexts allows improvements on the classification accuracy up to 10%. Finally, we highlight the applicability and the generality of our proposal in practice, pointing out this study as a promising research direction.     

Pairwise-adaptive dissimilarity measure for document clustering

This paper introduces a novel pairwise-adaptive dissimilarity measure for large high dimensional document datasets that improves the unsupervised clustering quality and speed compared to the original cosine dissimilarity measure. This measure dynamically selects a number of important features of the compared pair of document vectors. Two approaches for selecting the number of features in the application of the measure are discussed. The proposed feature selection process makes this dissimilarity measure especially applicable in large, high dimensional document collections. Its performance is validated on several test sets originating from standardized datasets. The dissimilarity measure is compared to the well-known cosine dissimilarity measure using the average F-measures of the hierarchical agglomerative clustering result. This new dissimilarity measure results in an improved clustering result obtained with a lower required computational time.     

Topic PageRank——一种基于主题的搜索引擎

通过研究传统的超链分析算法PageRank及其改进算法Hilltop和TSPR的不足，提出了一种新的改进的方法Topic PageRank。这种算法是对每一个页面进行页面分类，然后根据分类的结果分别对每一个主题进行页面等级计算，因此，每一个页面对不同的主题将呈现出不同的页面等级得分，能更加准确地反映出页面的重要性。     

基于话题标签的微博主题挖掘

随着互联网的发展,微博已成为人们获取信息的主要平台,为从海量微博中挖掘出有价值的主题信息,结合微博中的会话、转发和话题标签,将微博划分为用户兴趣、用户互动和话题微博3类,提出基于作者主题模型( ATM)的话题标签主题模型HC-ATM,使用Gibbs抽样法对模型进行推导,获取微博主题结构。在Twitter数据集上的实验结果表明,与ATM模型和基于潜在狄利克雷分布的微博生成模型相比, HC-ATM模型的主题困惑度更小、差异度更大,并且能有效挖掘出不同微博类型的主题分布。     

Topic correlation and individual influence analysis in online forums

Over the last few years, online forums have gained massive popularity and have become one of the most influential web social media in our times. The forum document corpus can be seemed to be composed of various topics evolved over time, and every topics is reflected on a volume of keywords and social actors. In this paper, we attempt to study the interesting problem: for the evolving topics, were there any correlation between them? We propose a method for discovering the dependency relationship between the topics of documents in adjacent time stamps based on the knowledge of content semantic similarity and social interactions of authors and repliers. We introduce mutual information measure to estimate the correlation between the topics. Applied to the realistic forum data, we show how topics are related and which postings can be recommended to another as similar topics. We also show how the authors impact the topics and propose a new way for evaluating author impact.     

Optimal spectral resolution in system identification

An optimisation approach to dynamic model identification is considered for frequency-response techniques. Identification accuracy is provided via optimal spectral resolution using a priori information. The optimal resolution is sought at each frequency using computer graphics in order to provide a compromise between bias and variance of the spectral estimates. The estimation procedure is demonstrated with simulation examples.     

专家寻找模型融合框架研究

网络中存在着规模庞大的信息,搜索引擎如Google为网络海量信息的检索提供了有效的途径,但是许多潜藏的知识仍然无法被搜索到。而且,大量知识并未存储于文档或者数据库中,其中大部分仅存在于人脑中。对于网络中无法检索到的知识,则需要找到掌握这些知识的专家,并通过交流获取这些知识。目前专家寻找的方法有语言模型、主题模型等,这些方法各有优缺点。提出一种专家寻找模型融合框架,该框架可有效地将已有的专家寻找模型结合起来,从而提高专家寻找的精确度与鲁棒性。实验结果支持了这一结论。     

Topic PageRank——一种基于主题的搜索引擎

通过研究传统的超链分析算法PageRank及其改进算法Hilltop和TSPR的不足,提出了一种新的改进的方法Topic PageRank。这种算法是对每一个页面进行页面分类,然后根据分类的结果分别对每一个主题进行页面等级计算,因此,每一个页面对不同的主题将呈现出不同的页面等级得分,能更加准确地反映出页面的重要性。     

A tool to discover the main themes in a Spanish or English document

While most work on Knowledge Discovery in databases has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form. In this paper a system based on information retrieval and text mining methods is presented. In addition, it is shown how the system analyzes a document containing natural language sentences in order to recognize its main topics or themes. The knowledge base used for the system is conformed by trees of concept. The architecture and the main algorithms of the system are discussed in this work.