Efficient phrase-based document indexing for Web document clustering

Document clustering techniques mostly rely on single term analysis of the document data set, such as the vector space model. To achieve more accurate document clustering, more informative features including phrases and their weights are particularly important in such scenarios. Document clustering is particularly useful in many applications such as automatic categorization of documents, grouping search engine results, building a taxonomy of documents, and others. This article presents two key parts of successful document clustering. The first part is a novel phrase-based document index model, the document index graph, which allows for incremental construction of a phrase-based index of the document set with an emphasis on efficiency, rather than relying on single-term indexes only. It provides efficient phrase matching that is used to judge the similarity between documents. The model is flexible in that it could revert to a compact representation of the vector space model if we choose not to index phrases. The second part is an incremental document clustering algorithm based on maximizing the tightness of clusters by carefully watching the pair-wise document similarity distribution inside clusters. The combination of these two components creates an underlying model for robust and accurate document similarity calculation that leads to much improved results in Web document clustering over traditional methods.     

Efficient Phrase-Based Document Similarity for Clustering

In this paper, we propose a phrase-based document similarity to compute the pair-wise similarities of documents based on the Suffix Tree Document (STD) model. By mapping each node in the suffix tree of STD model into a unique feature term in the Vector Space Document (VSD) model, the phrase-based document similarity naturally inherits the term tf-idf weighting scheme in computing the document similarity with phrases. We apply the phrase-based document similarity to the group-average Hierarchical Agglomerative Clustering (HAC) algorithm and develop a new document clustering approach. Our evaluation experiments indicate that, the new clustering approach is very effective on clustering the documents of two standard document benchmark corpora OHSUMED and RCV1. The quality of the clustering results significantly surpass the results of traditional single-word textit{tf-idf} similarity measure in the same HAC algorithm, especially in large document data sets. Furthermore, by studying the property of STD model, we conclude that the feature vector of phrase terms in the STD model can be considered as an expanded feature vector of the traditional single-word terms in the VSD model. This conclusion sufficiently explains why the phrase-based document similarity works much better than the single-word tf-idf similarity measure.     

基于文档标引图模型的文本相似度策略

文档标引图是一种基于短语的图结构文本特征表示模型,能更加全面、准确地表达文本特征信息,实现渐增的文本聚类和信息处理。该文基于文档标引图特征模型,提出文档相似度计算加法策略和乘法策略,采用变换函数对文档相似度值进行调整,增强文档之间的可区分性,改进文本聚类和分类等处理的性能,实例证明了策略的有效性。     

Anomaly detection in web documents using crisp and fuzzy-based cosine clustering methodology

Cluster analysis is a primary tool for detecting anomalous behavior in real-world data such as web documents, medical records of patients or other personal data. Most existing methods for document clustering are based on the classical vector-space model, which represents each document by a fixed-size vector of weighted key terms often referred to as key phrases. Since vector representations of documents are frequently very sparse, inverted files are used to prevent a tremendous computational overload which may be caused in large and diverse document collections such as pages downloaded from the World Wide Web. In order to reduce computation costs and space complexity, many popular methods for clustering web documents, including those using inverted files, usually assume a relatively small prefixed number of clusters.We propose several new crisp and fuzzy approaches based on the cosine similarity principle for clustering documents that are represented by variable-size vectors of key phrases, without limiting the final number of clusters. Each entry in a vector consists of two fields. The first field refers to a key phrase in the document and the second denotes an importance weight associated with this key phrase within the particular document. Removing the restriction on the total number of clusters, may moderately increase computing costs but on the other hand improves the method’s performance in classifying incoming vectors as normal or abnormal, based on their similarity to the existing clusters. All the procedures represented in this work are characterized by two features: (a) the number of clusters is not restricted by some relatively prefixed small number, i.e., an arbitrary new incoming vector which is not similar to any of the existing cluster centers necessarily starts a new cluster and (b) a vector with multiple appearance n in the training set is counted as n distinct vectors rather than a single vector. These features are the main reasons for the high quality performance of the proposed algorithms. We later describe them in detail and show their implementation in a real-world application from the area of web activity monitoring, in particular, by detecting anomalous documents downloaded from the internet by users with abnormal information interests.     

Statistical semantics for enhancing document clustering

Document clustering algorithms usually use vector space model (VSM) as their underlying model for document representation. VSM assumes that terms are independent and accordingly ignores any semantic relations between them. This results in mapping documents to a space where the proximity between document vectors does not reflect their true semantic similarity. This paper proposes new models for document representation that capture semantic similarity between documents based on measures of correlations between their terms. The paper uses the proposed models to enhance the effectiveness of different algorithms for document clustering. The proposed representation models define a corpus-specific semantic similarity by estimating measures of term–term correlations from the documents to be clustered. The corpus of documents accordingly defines a context in which semantic similarity is calculated. Experiments have been conducted on thirteen benchmark data sets to empirically evaluate the effectiveness of the proposed models and compare them to VSM and other well-known models for capturing semantic similarity.     

一种基于容错粗糙集的Web搜索结果聚类方法

一些Web聚类方法把类严格作为互斥的关系,聚类效果不理想．一种基于容错粗糙集的k均值的聚类解决了这一问题．首先运用向量模型表示Web文档信息,采用常规方法得到文本特征词集,然后利用某些特征词协同出现的价值,构造特征词客错关系,扩充特征词的描述能力,最后用特征词容错类描述文档之间的相似关系,实现了Web搜索结果聚类,并提出了简单直观的衡量聚类精度的T模型．实验结果表明,利用容错关系聚类的类标记描述性强、容易理解、明显优于普通k均值算法．     

一种基于混合神经网络的Web文档聚类算法

针对Web文档的特点,提出了一种多层向量空间模型,用来确定Web文档特征词的权重,然后给出了一种基于混合神经网络的文档聚类算法。实验结果表明,所提出的Web文档聚类算法具有较好的聚类特性,它能将与一个主题相关的Web文档较完全和准确地聚成一类。     

A multi-level matching method with hybrid similarity for document retrieval

This paper presents a multi-level matching method for document retrieval (DR) using a hybrid document similarity. Documents are represented by multi-level structure including document level and paragraph level. This multi-level-structured representation is designed to model underlying semantics in a more flexible and accurate way that the conventional flat term histograms find it hard to cope with. The matching between documents is then transformed into an optimization problem with Earth Mover’s Distance (EMD). A hybrid similarity is used to synthesize the global and local semantics in documents to improve the retrieval accuracy. In this paper, we have performed extensive experimental study and verification. The results suggest that the proposed method works well for lengthy documents with evident spatial distributions of terms.     

基于向量空间模型的文档聚类研究

文档聚类在Web文本挖掘中占有重要地位．是聚类分析在文本处理领域的应用。文章介绍了基于向量空间模型的文本表示方法,分析并优化了向量空间模型中特征词条权重的评价函数,使基于距离的相似性度量更为准确。重点分析了Web文档聚类中普遍使用的基于划分的k-means算法．对于k-means算法随机选取初始聚类中心的缺陷．详细介绍了采用基于最大最小距离法的原则,结合抽样技术思想,来稳定初始聚类中心的选取,改善聚类结果。     

基于词项—句子—文档三层图模型的多文档自动摘要

应用图模型来研究多文档自动摘要是当前研究的一个热点,它以句子为顶点,以句子之间相似度为边的权重构造无向图结构。由于此模型没有充分考虑句子中的词项权重信息以及句子所属的文档信息,针对这个问题,该文提出了一种基于词项—句子—文档的三层图模型,该模型可充分利用句子中的词项权重信息以及句子所属的文档信息来计算句子相似度。在DUC2003和DUC2004数据集上的实验结果表明,基于词项—句子—文档三层图模型的方法优于LexRank模型和文档敏感图模型。     

Rich document representation and classification: An analysis

There are three factors involved in text classification. These are classification model, similarity measure and document representation model. In this paper, we will focus on document representation and demonstrate that the choice of document representation has a profound impact on the quality of the classifier. In our experiments, we have used the centroid-based text classifier, which is a simple and robust text classification scheme. We will compare four different types of document representations: N-grams, Single terms, phrases and RDR which is a logic-based document representation. The N-gram representation is a string-based representation with no linguistic processing. The Single term approach is based on words with minimum linguistic processing. The phrase approach is based on linguistically formed phrases and single words. The RDR is based on linguistic processing and representing documents as a set of logical predicates. We have experimented with many text collections and we have obtained similar results. Here, we base our arguments on experiments conducted on Reuters-21578. We show that RDR, the more complex representation, produces more effective classifier on Reuters-21578, followed by the phrase approach.     

基于词条与语意差异度量的文档聚类算法

已有的文本聚类算法大多基于一般的相似性度量而忽略了语义内容,对此提出一种基于最大化文本判别信息的文本聚类算法。首先,分别分析词条对其类簇与其他类簇的判别信息,并且将数据集从输入空间转换至差异分数矩阵空间;然后,设计了一个贪婪算法来筛选矩阵每行的低分数词条;最终,采用最大似然估计对文本差别信息进行平滑处理。仿真实验结果表明,所提方法的文档聚类质量优于其他分层与单层聚类算法,并且具有较好的可解释性与收敛性。     

基于词条之间关联关系的文档聚类

针对现有的空间向量模型在进行文档表示时忽略词条之间的语义关系的不足,提出了一种新的基于关联规则的文档向量表示方法。在广义空间向量模型中分析词条的频繁同现关系得到词条同现语义,根据关联规则分析词条之间的关联相关性,挖掘出文档中词条之间的潜在关联语义关系,将词条同现语义和关联语义线性加权对文档进行表示。实验结果表明,与BOW模型和GVSM模型相比,采用关联规则文档向量表示的文档聚类结果更准确。     

A New Retrieval Model Based on TextTiling for Document Similarity Search

Document similarity search is to find documents similar to a given query document and return a ranked list of similar documents to users, which is widely used in many text and web systems, such as digital library, search engine, etc. Traditional retrieval models, including the Okapi's BM25 model and the Smart's vector space model with length normalization, could handle this problem to some extent by taking the query document as a long query. In practice, the Cosine measure is considered as the best model for document similarity search because of its good ability to measure similarity between two documents. In this paper, the quantitative performances of the above models are compared using experiments. Because the Cosine measure is not able to reflect the structural similarity between documents, a new retrieval model based on TextTiling is proposed in the paper. The proposed model takes into account the subtopic structures of documents. It first splits the documents into text segments with TextTiling and calculates the similarities for different pairs of text segments in the documents. Lastly the overall similarity between the documents is returned by combining the similarities of different pairs of text segments with optimal matching method. Experiments are performed and results show: 1) the popular retrieval models (the Okapi's BM25 model and the Smart's vector space model with length normalization) do not perform well for document similarity search; 2) the proposed model based on TextTiling is effective and outperforms other models, including the Cosine measure; 3) the methods for the three components in the proposed model are validated to be appropriately employed.     

CFV-NB：基于概念特征向量的NB文档分类模型 

提出了一种基于概念特征向量的NB文档分类方法。该方法在未标注文档集上通过SOM（Self-Organizing Maps）聚类产生若干初始文档类，并为每个文档类分配一个类标签，使用最大信息熵的方法建立每个文档类的概念特征向量。在概念特征向量空间上建立最终的文档分类器：CFB-NB。     

基于潜在语义分析的BBS文档Bayes鉴别器

电子公告栏(BBS)的滥用是一种以信息污染为特色的社会问题，对BBS文档进行鉴别已成为信息安全重要内容之一，该文融合了数据挖掘技术、数理统计技术和自然语言理解技术，提出了基于潜在语义分析与Bayes分类的BBS文档鉴别方法：利用自然语言处理技术从训练文档中抽取典型短语集；通过潜在语义分析进行典型短语同义归约，应用关联规则采掘技术提高典型短语间的独立性；用Bayes分类器对BBS文档进行鉴别。该文还对影响系统的关键参数进行了大量的讨论和测试，实验表明该方法对于BBS文档的鉴别是可行而有效的。     

Text document clustering using global term context vectors

Despite the advantages of the traditional vector space model (VSM) representation, there are known deficiencies concerning the term independence assumption. The high dimensionality and sparsity of the text feature space and phenomena such as polysemy and synonymy can only be handled if a way is provided to measure term similarity. Many approaches have been proposed that map document vectors onto a new feature space where learning algorithms can achieve better solutions. This paper presents the global term context vector-VSM (GTCV-VSM) method for text document representation. It is an extension to VSM that: (i) it captures local contextual information for each term occurrence in the term sequences of documents; (ii) the local contexts for the occurrences of a term are combined to define the global context of that term; (iii) using the global context of all terms a proper semantic matrix is constructed; (iv) this matrix is further used to linearly map traditional VSM (Bag of Words—BOW) document vectors onto a ‘semantically smoothed’ feature space where problems such as text document clustering can be solved more efficiently. We present an experimental study demonstrating the improvement of clustering results when the proposed GTCV-VSM representation is used compared with traditional VSM-based approaches.     

A new algorithm for clustering search results

We develop a new algorithm for clustering search results. Differently from many other clustering systems that have been recently proposed as a post-processing step for Web search engines, our system is not based on phrase analysis inside snippets, but instead uses latent semantic indexing on the whole document content. A main contribution of the paper is a novel strategy – called dynamic SVD clustering – to discover the optimal number of singular values to be used for clustering purposes. Moreover, the algorithm is such that the SVD computation step has in practice good performance, which makes it feasible to perform clustering when term vectors are available. We show that the algorithm has very good classification performance, and that it can be effectively used to cluster results of a search engine to make them easier to browse by users. The algorithm has being integrated into the Noodles search engine, a tool for searching and clustering Web and desktop documents.     

结合统计特征和图模型的半监督式中文关键短语抽取方法

关键短语抽取,即从文档中抽取能够表达文档主题和内容的关键短语集合,对于信息检索和文档分类等文本处理任务具有重要意义。然而,现有文献缺乏针对中文特点的关键短语抽取算法的研究。为此,该文提出了一种半监督式中文关键短语抽取模型,该模型采用预训练语言模型来表征短语及文章,以减少算法对大量标注训练数据的依赖;进而提出图模型描述候选短语间的相似性空间并迭代计算各短语的重要度;同时结合了多项统计特征来进一步提高短语评估的准确率。对比实验表明,该文提出的方法在中文关键短语抽取方面比基线方法具有明显的提升效果。     

英汉翻译中短语的识别与分析

本文在讨论了英文事的形态结构特征的基础上,提出了一种以短语模式空间匹配为基础的短语识别算法。该算法通过对短语的原型描述与输入文本中的全部可能路径进行递增模式匹配来识别具有外部形态约束和框架结构的复杂短语构。本文最后讨论了短语模板和基于复杂特征的短语描述方式。