组织知识结构构建方法及在专利文献中的运用

组织知识结构是组织中知识的基本构成及其相互关系的反映,但目前学者们较少关注组织知识结构构建方法的研究。针对这一问题,本文首先从宏观和微观两个层面明确组织知识结构的概念,然后以物质载体中的专利文献为例,使用文本挖掘的方法对组织知识结构的构建方法进行研究。该方法首先抽取专利文献中的知识元,进而采用层次凝聚聚类算法(HAC)对这些知识元进行层次聚类,并基于可视化方法形成组织知识结构。最后,以某组织的专利文献背景对其组织知识结构构建方法进行了实例验证,取得了良好的效果。     

A scaleable document clustering approach for large document corpora

In this paper, the scalability and quality of the contextual document clustering (CDC) approach is demonstrated for large data-sets using the whole Reuters Corpus Volume 1 (RCV1) collection. CDC is a form of distributional clustering, which automatically discovers contexts of narrow scope within a document corpus. These contexts act as attractors for clustering documents that are semantically related to each other. Once clustered, the documents are organized into a minimum spanning tree so that the topical similarity of adjacent documents within this structure can be assessed. The pre-defined categories from three different document category sets are used to assess the quality of CDC in terms of its ability to group and structure semantically related documents given the contexts. Quality is evaluated based on two factors, the category overlap between adjacent documents within a cluster, and how well a representative document categorizes all the other documents within a cluster. As the RCV1 collection was collated in a time ordered fashion, it was possible to assess the stability of clusters formed from documents within one time interval when presented with new unseen documents at subsequent time intervals. We demonstrate that CDC is a powerful and scaleable technique with the ability to create stable clusters of high quality. Additionally, to our knowledge this is the first time that a collection as large as RCV1 has been analyzed in its entirety using a static clustering approach.     

文本聚类算法的质量评价

文本聚类是建立大规模文本集合的分类体系实例的有效手段之一。本文讨论了利用标准的分类测试集合进行聚类质量的量化评价的手段，选择了k-Means聚类算法、STC（后缀树聚类）算法和基于Ant的聚类算法进行了实验对比。对实验结果的分析表明，STC聚类算法由于在处理文本时充分考虑了文本的短语特性，其聚类效果较好；基于Ant的聚类算法的结果受参数输入的影响较大；在Ant聚类算法中引入文本特性可以提高聚类结果的质量。     

Clustering tagged documents with labeled and unlabeled documents

This study employs our proposed semi-supervised clustering method called Constrained-PLSA to cluster tagged documents with a small amount of labeled documents and uses two data sets for system performance evaluations. The first data set is a document set whose boundaries among the clusters are not clear; while the second one has clear boundaries among clusters. This study employs abstracts of papers and the tags annotated by users to cluster documents. Four combinations of tags and words are used for feature representations. The experimental results indicate that almost all of the methods can benefit from tags. However, unsupervised learning methods fail to function properly in the data set with noisy information, but Constrained-PLSA functions properly. In many real applications, background knowledge is ready, making it appropriate to employ background knowledge in the clustering process to make the learning more fast and effective.     

Network text analysis: A two-way classification approach

Text clustering is a well-known method for information retrieval and numerous methods for classifying words, documents or both together have been proposed. Frequently, textual data are encoded using vector models so the corpus is transformed in to a matrix of terms by documents; using this representation text clustering generates groups of similar objects on the basis of the presence/absence of the words in the documents. An alternative way to work on texts is to represent them as a network where nodes are entities connected by the presence and distribution of the words in the documents. In this work, after summarising the state of the art of text clustering we will present a new network approach to textual data. We undertake text co-clustering using methods developed for social network analysis. Several experimental results will be presented to demonstrate the validity of the approach and the advantages of this technique compared to existing methods.     

Two scalable algorithms for associative text classification

Associative classification methods have been recently applied to various categorization tasks due to its simplicity and high accuracy. To improve the coverage for test documents and to raise classification accuracy, some associative classifiers generate a huge number of association rules during the mining step. We present two algorithms to increase the computational efficiency of associative classification: one to store rules very efficiently, and the other to increase the speed of rule matching, using all of the generated rules. Empirical results using three large-scale text collections demonstrate that the proposed algorithms increase the feasibility of applying associative classification to large-scale problems.     

Exploration of textual document archives using a fuzzy hierarchical clustering algorithm in the GAMBAL system

The Internet, together with the large amount of textual information available in document archives, has increased the relevance of information retrieval related tools. In this work we present an extension of the Gambal system for clustering and visualization of documents based on fuzzy clustering techniques. The tool allows to structure the set of documents in a hierarchical way (using a fuzzy hierarchical structure) and represent this structure in a graphical interface (a 3D sphere) over which the user can navigate.Gambal allows the analysis of the documents and the computation of their similarity not only on the basis of the syntactic similarity between words but also based on a dictionary (Wordnet 1.7) and latent semantics analysis.     

Optimization and label propagation in bipartite heterogeneous networks to improve transductive classification of texts

Transductive classification is a useful way to classify texts when labeled training examples are insufficient. Several algorithms to perform transductive classification considering text collections represented in a vector space model have been proposed. However, the use of these algorithms is unfeasible in practical applications due to the independence assumption among instances or terms and the drawbacks of these algorithms. Network-based algorithms come up to avoid the drawbacks of the algorithms based on vector space model and to improve transductive classification. Networks are mostly used for label propagation, in which some labeled objects propagate their labels to other objects through the network connections. Bipartite networks are useful to represent text collections as networks and perform label propagation. The generation of this type of network avoids requirements such as collections with hyperlinks or citations, computation of similarities among all texts in the collection, as well as the setup of a number of parameters. In a bipartite heterogeneous network, objects correspond to documents and terms, and the connections are given by the occurrences of terms in documents. The label propagation is performed from documents to terms and then from terms to documents iteratively. Nevertheless, instead of using terms just as means of label propagation, in this article we propose the use of the bipartite network structure to define the relevance scores of terms for classes through an optimization process and then propagate these relevance scores to define labels for unlabeled documents. The new document labels are used to redefine the relevance scores of terms which consequently redefine the labels of unlabeled documents in an iterative process. We demonstrated that the proposed approach surpasses the algorithms for transductive classification based on vector space model or networks. Moreover, we demonstrated that the proposed algorithm effectively makes use of unlabeled documents to improve classification and it is faster than other transductive algorithms.     

Re-ranking model based on document clusters

In this paper, we describe a model of information retrieval system that is based on a document re-ranking method using document clusters. In the first step, we retrieve documents based on the inverted-file method. Next, we analyze the retrieved documents using document clusters, and re-rank them. In this step, we use static clusters and dynamic cluster view. Consequently, we can produce clusters that are tailored to characteristics of the query. We focus on the merits of the inverted-file method and cluster analysis. In other words, we retrieve documents based on the inverted-file method and analyze all terms in document based on the cluster analysis. By these two steps, we can get the retrieved results which are made by the consideration of the context of all terms in a document as well as query terms. We will show that our method achieves significant improvements over the method based on similarity search ranking alone.     

Testing the cluster hypothesis in distributed information retrieval

How to merge and organise query results retrieved from different resources is one of the key issues in distributed information retrieval. Some previous research and experiments suggest that cluster-based document browsing is more effective than a single merged list. Cluster-based retrieval results presentation is based on the cluster hypothesis, which states that documents that cluster together have a similar relevance to a given query. However, while this hypothesis has been demonstrated to hold in classical information retrieval environments, it has never been fully tested in heterogeneous distributed information retrieval environments. Heterogeneous document representations, the presence of document duplicates, and disparate qualities of retrieval results, are major features of an heterogeneous distributed information retrieval environment that might disrupt the effectiveness of the cluster hypothesis. In this paper we report on an experimental investigation into the validity and effectiveness of the cluster hypothesis in highly heterogeneous distributed information retrieval environments. The results show that although clustering is affected by different retrieval results representations and quality, the cluster hypothesis still holds and that generating hierarchical clusters in highly heterogeneous distributed information retrieval environments is still a very effective way of presenting retrieval results to users.     

基于量子自组织网络的Web文本自动分类方法

针对Web信息挖掘中的文本自动分类问题,提出了一种基于模糊特征向量和量子自组织特征映射网络的分类方法.该方法在特征提取时充分考虑了特征项在文档中的Web位置信息,构造出模糊特征向量,使自动分类原则更接近手工分类方法.     

基于模糊向量空间的文本分类方法

本文针对文本自动分类问题,提出了一种基于模糊向量空间模型和径向基函数网络的分类方法。网络由输入层、隐层和输出层组成。输入层完成分类样本的输入,隐层提取输入样本所隐含的模式特征,将分类结果在输出层表现出来。该方法在特征提取时充分考虑了特征项在文档中的位置信息,构造出模糊特征向量,使自动分类更接近手工分类方法。以中国期刊网全文数据库部分文档数据为例验证了该方法的有效性。     

基于信息素的蚁群聚类算法

数据挖掘是在海量的数据中寻找模式或规则的过程。数据挖掘强调的是发现知识,获得的知识类型包括关联规则、分类、回归、聚类、依赖模型等。本文对其中聚类分析技术进行了较为深入地研究与分析,提出基于信息素的蚁群聚类算法PCBP,利用信息素指导蚂蚁移动,用不同速度的蚂蚁来改善聚类质量,并在这个思想基础上提出层次化蚁群聚类方法。为人们研究聚类提供了新思路和新途径,因此本文的研究具有一定的理论和实践意义。     

Deciphering cluster representations

There are several recent studies that propose search output clustering as an alternative representation method to ranked output. Users are provided with cluster representations instead of lists of titles and invited to make decisions on groups of documents. This paper discusses the difficulties involved in representing clusters for users’ evaluation in a concise but easily interpretable form. The discussion is based on findings and user feedback from a user study investigating the effectiveness of search output clustering. The overall impression created by the experiment results and users’ feedback is that clusters cannot be relied on to consistently produce meaningful document groups that can easily be recognised by the users. They also seem to lead to unrealistic user expectations.     

基于游记主题挖掘与表达的旅游信息推荐研究

[目的/意义]针对在线旅游平台,提出一种挖掘游记主题标签,以代表性游记以及其中相关内容进行旅游信息推荐的新策略。[方法/过程]在利用文本挖掘技术,构建LDA主题模型,形成游记文本主题标签的基础上,通过游记代表度算法,筛选出针对相应标签的高描述度与高忠诚度游记进行旅游信息推荐,以客观表达文本聚类结果以及主题词之间的语义关系,并以蚂蜂窝旅游网中的"杭州游记"为例,加以验证。[结果/结论]结果表明,这种方式能挖掘出旅游者在历史旅游经历中真实的旅游热点及重点信息需求,针对高相似度游记的识别与聚类具有良好效果,对旅游信息细粒度推荐具有指导意义与实践意义。     

Fuzzy topic modeling approach for text mining over short text

In this era, the proliferating role of social media in our lives has popularized the posting of the short text. The short texts contain limited context with unique characteristics which makes them difficult to handle. Every day billions of short texts are produced in the form of tags, keywords, tweets, phone messages, messenger conversations social network posts, etc. The analysis of these short texts is imperative in the field of text mining and content analysis. The extraction of precise topics from large-scale short text documents is a critical and challenging task. The conventional approaches fail to obtain word co-occurrence patterns in topics due to the sparsity problem in short texts, such as text over the web, social media like Twitter, and news headlines. Therefore, in this paper, the sparsity problem is ameliorated by presenting a novel fuzzy topic modeling (FTM) approach for short text through fuzzy perspective. In this research, the local and global term frequencies are computed through a bag-of-words (BOW) model. To remove the negative impact of high dimensionality on the global term weighting, the principal component analysis is adopted; thereafter the fuzzy c-means algorithm is employed to retrieve the semantically relevant topics from the documents. The experiments are conducted over the three real-world short text datasets: the snippets dataset is in the category of small dataset whereas the other two datasets, Twitter and questions, are the bigger datasets. Experimental results show that the proposed approach discovered the topics more precisely and performed better as compared to other state-of-the-art baseline topic models such as GLTM, CSTM, LTM, LDA, Mix-gram, BTM, SATM, and DREx+LDA. The performance of FTM is also demonstrated in classification, clustering, topic coherence and execution time. FTM classification accuracy is 0.95, 0.94, 0.91, 0.89 and 0.87 on snippets dataset with 50, 75, 100, 125 and 200 number of topics. The classification accuracy of FTM on questions dataset is 0.73, 0.74, 0.70, 0.68 and 0.78 with 50, 75, 100, 125 and 200 number of topics. The classification accuracies of FTM on snippets and questions datasets are higher than state-of-the-art baseline topic models.     

An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit

Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures.     

Improving document clustering in a learned concept space

Most document clustering algorithms operate in a high dimensional bag-of-words space. The inherent presence of noise in such representation obviously degrades the performance of most of these approaches. In this paper we investigate an unsupervised dimensionality reduction technique for document clustering. This technique is based upon the assumption that terms co-occurring in the same context with the same frequencies are semantically related. On the basis of this assumption we first find term clusters using a classification version of the EM algorithm. Documents are then represented in the space of these term clusters and a multinomial mixture model (MM) is used to build document clusters. We empirically show on four document collections, Reuters-21578, Reuters RCV2-French, 20Newsgroups and WebKB, that this new text representation noticeably increases the performance of the MM model. By relating the proposed approach to the Probabilistic Latent Semantic Analysis (PLSA) model we further propose an extension of the latter in which an extra latent variable allows the model to co-cluster documents and terms simultaneously. We show on these four datasets that the proposed extended version of the PLSA model produces statistically significant improvements with respect to two clustering measures over all variants of the original PLSA and the MM models.