A review of topic modeling methods

Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs.     

TCBR-HMM: An HMM-based text classifier with a CBR system

This paper presents an innovative solution to model distributed adaptive systems in biomedical environments. We present an original TCBR-HMM (Text Case Based Reasoning-Hidden Markov Model) for biomedical text classification based on document content. The main goal is to propose a more effective classifier than current methods in this environment where the model needs to be adapted to new documents in an iterative learning frame. To demonstrate its achievement, we include a set of experiments, which have been performed on OSHUMED corpus. Our classifier is compared with Naive Bayes and SVM techniques, commonly used in text classification tasks. The results suggest that the TCBR-HMM Model is indeed more suitable for document classification. The model is empirically and statistically comparable to the SVM classifier and outperforms it in terms of time efficiency.     

Analysis and visualisation with host-based software – The features of STNAnaVist™

This article is based on experiences with data and text mining to gain information for strategic business decisions, using host-based analysis and visualisation (A/V), primarily in the field of patents. The potential advantages of host-based A/V are pointed out and the features of the first such A/V software, STN^®AnaVist™, are described in detail. Areas covered include the user interfaces, initial set of documents for A/V, data mining, text mining, reporting, and suggestions for further development.     

Class normalization in centroid-based text categorization

Centroid-based categorization is one of the most popular algorithms in text classification. In this approach, normalization is an important factor to improve performance of a centroid-based classifier when documents in text collection have quite different sizes and/or the numbers of documents in classes are unbalanced. In the past, most researchers applied document normalization, e.g., document-length normalization, while some consider a simple kind of class normalization, so-called class-length normalization, to solve the unbalancedness problem. However, there is no intensive work that clarifies how these normalizations affect classification performance and whether there are any other useful normalizations. The purpose of this paper is three folds; (1) to investigate the effectiveness of document- and class-length normalizations on several data sets, (2) to evaluate a number of commonly used normalization functions and (3) to introduce a new type of class normalization, called term-length normalization, which exploits term distribution among documents in the class. The experimental results show that a classifier with weight-merge-normalize approach (class-length normalization) performs better than one with weight-normalize-merge approach (document-length normalization) for the data sets with unbalanced numbers of documents in classes, and is quite competitive for those with balanced numbers of documents. For normalization functions, the normalization based on term weighting performs better than the others on average. For term-length normalization, it is useful for improving classification accuracy. The combination of term- and class-length normalizations outperforms pure class-length normalization and pure term-length normalization as well as unnormalization with the gaps of 4.29%, 11.50%, 30.09%, respectively.     

文本索引词项相对权重计算方法与应用

文本索引词权重计算方法决定了文本分类的准确率。该文提出一种文本索引词项相对权重计算方法,即文本索引词项权重根据索引词项在该文本中的出现频率与在整个文本空间出现的平均频率之间的相对值进行计算。该方法能有效地提高索引词对文本内容识别的准确性。     

Computer software as text

Participatory design strategies have placed the user at the center of the design process, leaving computer software designers without guidelines for how to design and implement the software system. This paper aims to bring the designer back to the center of the design process. and the way of doing it is to consider computer software as text. Three different text theories are presented in order to explain what is meant by text, namely pragmatics, structuralism and deconstructivism. Finally it is discussed how the design processes should be understood, and how they should be organized when taking the text point of view of computer software.     

Formatting texts accessed randomly

Full-text systems that access text randomly cannot normally determine the format operations in effect for a given target location. The problem can be solved by viewing the format marks as the non-terminals in a format grammar. A formatted text can then be parsed using the grammar to build a data structure that serves both as a parse tree and as a search tree. While processing a retrieved segment, a full-text system can follow the search tree from root to leaf, collecting the format marks encountered at each node to derive the sequence of commands active for that segment. The approach also supports the notion of a ‘well formatted’ document and provides a means for verifying the well-formedness of a given text. To illustrate the approach, a sample set of format marks and a sample grammar are given suitable for formatting and parsing the article as a sample text.     

基于SNS的网络挖掘系统研究

介绍网络挖掘系统的功能模型及其采用的体系结构．并重点阐述系统采用的社团发现技术、文本提取和文本分类等关键技术,并对存在的问题进行分析和总结。     

Text classification using genetic algorithm oriented latent semantic features

In this paper, genetic algorithm oriented latent semantic features (GALSF) are proposed to obtain better representation of documents in text classification. The proposed approach consists of feature selection and feature transformation stages. The first stage is carried out using the state-of-the-art filter-based methods. The second stage employs latent semantic indexing (LSI) empowered by genetic algorithm such that a better projection is attained using appropriate singular vectors, which are not limited to the ones corresponding to the largest singular values, unlike standard LSI approach. In this way, the singular vectors with small singular values may also be used for projection whereas the vectors with large singular values may be eliminated as well to obtain better discrimination. Experimental results demonstrate that GALSF outperforms both LSI and filter-based feature selection methods on benchmark datasets for various feature dimensions.     

正则表达式在Word中的应用

微软在Word 2000及后续所有版本中,提供基于正则表达式的查找替换功能,可对文中符合正则表达式规则的文字实现一次定位选中,并可对查找到的文字按照替换表达式进行批量替换。Word正则表达式提供包括转义符、限定符、定位符、字符集合、分组符、反向引用等元字符功能。Word正则表达式可结合文本格式功能实现联合查找。