A new dual wing harmonium model for document retrieval

A new dual wing harmonium model that integrates term frequency features and term connection features into a low dimensional semantic space without increase of computation load is proposed for the application of document retrieval. Terms and vectorized graph connectionists are extracted from the graph representation of document by employing weighted feature extraction method. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification, and the comparative results suggest that the proposed method is accurate and computationally efficient for document retrieval.     

Topic identification based on document coherence and spectral analysis

In a world with vast information overload, well-optimized retrieval of relevant information has become increasingly important. Dividing large, multiple topic spanning documents into sets of coherent subdocuments facilitates the information retrieval process. This paper presents a novel technique to automatically subdivide a textual document into consistent components based on a coherence quantification function. This function is based on stem or term chains linking document entities, such as sentences or paragraphs, based on the reoccurrences of stems or terms. Applying this function on a document results in a coherence graph of the document linking its entities. Spectral graph partitioning techniques are used to divide this coherence graph into a number of subdocuments. A novel technique is introduced to obtain the most suitable number of subdocuments. These subdocuments are an aggregation of (not necessarily adjacent) entities. Performance tests are conducted in test environments based on standardized datasets to prove the algorithm’s capabilities. The relevance of these techniques for information retrieval and text mining is discussed.     

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.     

An efficient concept-based retrieval model for enhancing text retrieval quality

Most of the common techniques in text retrieval are based on the statistical analysis terms (words or phrases). Statistical analysis of term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that represent the concepts of the sentence, which leads to discovering the topic of the document. In this paper, a new concept-based retrieval model is introduced. The proposed concept-based retrieval model consists of conceptual ontological graph (COG) representation and concept-based weighting scheme. The COG representation captures the semantic structure of each term within a sentence. Then, all the terms are placed in the COG representation according to their contribution to the meaning of the sentence. The concept-based weighting analyzes terms at the sentence and document levels. This is different from the classical approach of analyzing terms at the document level only. The weighted terms are then ranked, and the top concepts are used to build a concept-based document index for text retrieval. The concept-based retrieval model can effectively discriminate between unimportant terms with respect to sentence semantics and terms which represent the concepts that capture the sentence meaning. Experiments using the proposed concept-based retrieval model on different data sets in text retrieval are conducted. The experiments provide comparison between traditional approaches and the concept-based retrieval model obtained by the combined approach of the conceptual ontological graph and the concept-based weighting scheme. The evaluation of results is performed using three quality measures, the preference measure (bpref), precision at 10 documents retrieved (P(10)) and the mean uninterpolated average precision (MAP). All of these quality measures are improved when the newly developed concept-based retrieval model is used, confirming that such model enhances the quality of text retrieval.     

The hybrid representation model for web document classification

Most Web content categorization methods are based on the vector space model of information retrieval. One of the most important advantages of this representation model is that it can be used by both instance‐based and model‐based classifiers. However, this popular method of document representation does not capture important structural information, such as the order and proximity of word occurrence or the location of a word within the document. It also makes no use of the markup information that can easily be extracted from the Web document HTML tags. A recently developed graph‐based Web document representation model can preserve Web document structural information. It was shown to outperform the traditional vector representation using the k‐Nearest Neighbor (k‐NN) classification algorithm. The problem, however, is that the eager (model‐based) classifiers cannot work with this representation directly. In this article, three new hybrid approaches to Web document classification are presented, built upon both graph and vector space representations, thus preserving the benefits and overcoming the limitations of each. The hybrid methods presented here are compared to vector‐based models using the C4.5 decision tree and the probabilistic Naïve Bayes classifiers on several benchmark Web document collections. The results demonstrate that the hybrid methods presented in this article outperform, in most cases, existing approaches in terms of classification accuracy, and in addition, achieve a significant reduction in the classification time. © 2008 Wiley Periodicals, Inc.     

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.     

基于词重要性的信息检索图模型

在信息检索建模中,确定索引词项在文档中的重要性是一项重要内容。以词袋(bag-of-word)的形式表示文档来建立检索模型的方法中大多是基于词项独立性假设,用TF和IDF的函数来计算词项的重要性,并未考虑词项之间的关系。该文采用基于词项图(graph-of-word)的文档表示形式来捕获词项间的依赖关系,提出了一种新的基于词重要性的信息检索图模型TI-IDF。根据词项图得到文档中词项的共现矩阵和词项间的概率转移矩阵,通过马尔科夫链计算方法来确定词项在文档中的重要性(Term Importance, TI),并以此替代索引过程中传统的词项频率TF。该模型具有更好的鲁棒性,我们在国际公开数据集上与传统的检索模型进行了比较。实验结果表明,该文提出的模型都要优于BM25,且在大多数情况下优于BM25的扩展模型、TW-IDF等模型。     

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.     

基于贝叶斯网络的结构化文档检索模型

研究表明合理考虑术语之间的关系可以提高检索系统的性能。采用共现分析的方法从文档集合中学习得到术语之间的关系,并应用到结构化文档检索中,提出了一个基于贝叶斯网络的结构化文档检索模型,给出了其拓扑结构、概率估计以及推理过程。实验表明该模型的检索性能要优于没有考虑术语之间关系的模型。     

Narrowing the semantic gap - improved text-based web document retrieval using visual features

We present the results of our work that seek to negotiate the gap between low-level features and high-level concepts in the domain of web document retrieval. This work concerns a technique, called the latent semantic indexing (LSI), which has been used for textual information retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. In this paper, we examine the use of this technique for content-based web document retrieval, using both keywords and image features to represent the documents. Two different approaches to image feature representation, namely, color histograms and color anglograms, are adopted and evaluated. Experimental results show that LSI, together with both textual and visual features, is able to extract the underlying semantic structure of web documents, thus helping to improve the retrieval performance significantly, even when querying is done using only keywords.     

基于改进卡方统计量的藏文文本表示方法

藏文文本表示是将非结构化的藏文文本转换为计算机能够处理的数据形式,是藏文文本分类、文本聚类等领域特征抽取的前提。传统的藏文文本表示方法较少考虑特征项之间的关联度,容易造成语义损失。为此,结合向量空间模型,提出一种新的藏文文本表示方法。提取文本中词频统计TF-IDF值较高的部分词项作为对比词项,对藏文文本进行断句处理,以每个句子作为一个语境主题,利用卡方统计量计算文本中词项与对比词项的关联程度。实验结果表明,与传统的向量空间模型相比,该方法能更准确地表示藏文文本。     

A comparison of a network structure and a database system used for document retrieval

Database systems have many advantages for implementing document retrieval systems. One of the main advantages would be the integration of data and text handling in a single information system. However, it has not been clear how much a database implementation would cost in terms of efficiency. In this paper, we compare a database implementation and a stand-alone implementation of a flexible representation of the content of documents and the associated search strategies. The representation used is a network of document and index term nodes. The comparison shows that certain features of a database system can have a significant effect on the efficiency of the implementation. Despite this, it appears that a database implementation of a sophisticated document retrieval system can be competitive with a stand-alone implementation.     

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.     

Chinese document re-ranking based on automatically acquired term resource

In this paper, we address the problem of document re-ranking in information retrieval, which is usually conducted after initial retrieval to improve rankings of relevant documents. To deal with this problem, we propose a method which automatically constructs a term resource specific to the document collection and then applies the resource to document re-ranking. The term resource includes a list of terms extracted from the documents as well as their weighting and correlations computed after initial retrieval. The term weighting based on local and global distribution ensures the re-ranking not sensitive to different choices of pseudo relevance, while the term correlation helps avoid any bias to certain specific concept embedded in queries. Experiments with NTCIR3 data show that the approach can not only improve performance of initial retrieval, but also make significant contribution to standard query expansion.     

基于层级匹配的维吾尔文关键词文档图像检索

为实现基于关键词的维吾尔文文档图像检索,提出一种基于由粗到细层级匹配的关键词文档图像检索方法。使用改进的投影切分法将经过预处理的文档图像切分成单词图像库,使用模板匹配对关键词进行粗匹配;在粗匹配的基础上,提取单词图像的方向梯度直方图(HOG)特征向量;通过支持向量机(SVM)分类器学习特征向量,实现关键词图像检索。在包含108张文档图像的数据库中进行实验,实验结果表明,检索准确率平均值为91.14%,召回率平均值为79.31%,该方法能有效实现基于关键词的维吾尔文文档图像检索。     

Negations and document length in logical retrieval

An unsolved problem in logic-based information retrieval is how to obtain automatically logical representations for documents and queries. This problem limits the impact of logical models for information retrieval because their full expressive power cannot be harnessed. In this paper we propose a method for producing logical document representations which goes further than other simplistic “bag-of-words” approaches. The suggested procedure adopts popular information retrieval heuristics, such as document length corrections and global term distribution. This work includes a report of several experiments applying partial document representations in the context of a propositional model of information retrieval. The benefits of this expressive framework, powered by the new logical indexing approach, become apparent in the evaluation.     

一种基于文档拓扑的相似性搜索算法

从海量文档中快速有效地搜索到相似文档是一个重要且耗时的问题。现有的文档相似性搜索算法是先找出候选文档集,再对候选文档进行相关性排序,找出最相关的文档。提出了一种基于文档拓扑的相似性搜索算法——Hub-N,将文档相似性搜索问题转化为图搜索问题,应用相应的剪枝技术,缩小了扫描文档的范围,提高了搜索效率。通过实验验证了算法的有效性和可行性。     

Semantic relation based personalized ranking approach for engineering document retrieval

Since engineering design is heavily informational, engineers want to retrieve existing engineering documents accurately during the product development process. However, engineers have difficulties searching for documents because of low retrieval accuracy. One of the reasons for this is the limitation of existing document ranking approaches, in which relationships between terms in documents are not considered to assess the relevance of the retrieved documents. Therefore, we propose a new ranking approach that provides more correct evaluation of document relevance to a given query. Our approach exploits domain ontology to consider relationships among terms in the relevance scoring process. Based on domain ontology, the semantics of a document are represented by a graph (called Document Semantic Network) and, then, proposed relation-based weighting schemes are used to evaluate the graph to calculate the document relevance score. In our ranking approach, user interests and searching intent are also considered in order to provide personalized services. The experimental results show that the proposed approach outperforms existing ranking approaches. A precisely represented semantics of a document as a graph and multiple relation-based weighting schemes are important factors underlying the notable improvement.     

Recent advances in graph-based pattern recognition with applications in document analysis

Graphs are a powerful and popular representation formalism in pattern recognition. Particularly in the field of document analysis they have found widespread application. From the formal point of view, however, graphs are quite limited in the sense that the majority of mathematical operations needed to build common algorithms, such as classifiers or clustering schemes, are not defined. Consequently, we observe a severe lack of algorithmic procedures that can directly be applied to graphs. There exists recent work, however, aimed at overcoming these limitations. The present paper first provides a review of the use of graph representations in document analysis. Then we discuss a number of novel approaches suitable for making tools from statistical pattern recognition available to graphs. These novel approaches include graph kernels and graph embedding. With several experiments, using different data sets from the field of document analysis, we show that the new methods have great potential to outperform traditional procedures applied to graph representations.