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。     

Near-duplicate document image matching: A graphical perspective

A near-duplicate document image matching approach characterized by a graphical perspective is proposed in this paper. Document images are represented by graphs whose nodes correspond to the objects in the images. Consequently, the image matching problem is then converted to graph matching. To deal with the instability of object segmentation, a multi-granularity object tree is constructed for a document image. Each level in the tree corresponds to one possible object segmentation, while different levels are characterized by various object granularities. Some graphs can be generated from the tree and the objects associated with each graph may be of different granularities. Two graphs with the maximum similarity are found from the multi-granularity object trees of the two near-duplicate document images which are to be matched. The encouraging experimental results have demonstrated the effectiveness of the proposed approach.     

Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction

Sentiment analysis, which detects the subjectivity or polarity of documents, is one of the fundamental tasks in text data analytics. Recently, the number of documents available online and offline is increasing dramatically, and preprocessed text data have more features. This development makes analysis more complex to be analyzed effectively. This paper proposes a novel semi-supervised Laplacian eigenmap (SS-LE). The SS-LE removes redundant features effectively by decreasing detection errors of sentiments. Moreover, it enables visualization of documents in perceptible low dimensional embedded space to provide a useful tool for text analytics. The proposed method is evaluated using multi-domain review data set in sentiment visualization and classification by comparing other dimensionality reduction methods. SS-LE provides a better similarity measure in the visualization result by separating positive and negative documents properly. Sentiment classification models trained over reduced data by SS-LE show higher accuracy. Overall, experimental results suggest that SS-LE has the potential to be used to visualize documents for the ease of analysis and to train a predictive model in sentiment analysis. SS-LE can also be applied to any other partially annotated text data sets.     

KHATT: An open Arabic offline handwritten text database

A comprehensive Arabic handwritten text database is an essential resource for Arabic handwritten text recognition research. This is especially true due to the lack of such database for Arabic handwritten text. In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) consisting of 1000 handwritten forms written by 1000 distinct writers from different countries. The forms were scanned at 200, 300, and 600 dpi resolutions. The database contains 2000 randomly selected paragraphs from 46 sources, 2000 minimal text paragraph covering all the shapes of Arabic characters, and optionally written paragraphs on open subjects. The 2000 random text paragraphs consist of 9327 lines. The database forms were randomly divided into 70%, 15%, and 15% sets for training, testing, and verification, respectively. This enables researchers to use the database and compare their results. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. The verified ground truth database contains meta-data describing the written text at the page, paragraph, and line levels in text and XML formats. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. In addition we are presenting our experimental results on the database using two classifiers, viz. Hidden Markov Models (HMM) and our novel syntactic classifier.     

Using ontology-based text classification to assist Job Hazard Analysis

The dangers of the construction industry due to the risk of fatal hazards, such as falling from extreme heights, being struck by heavy equipment or materials, and the possibility of electrocution, are well known. The concept of Job Hazard Analysis is commonly used to mitigate and control these occupational hazards. This technique analyzes the major tasks in a construction activity, identifies all potential task-related hazards, and suggests safe approaches to reduce or avoid each of these hazards. In this paper, the authors explore the possibility of leveraging existing construction safety resources to assist JHA, aiming to reduce the level of human effort required. Specifically, the authors apply ontology-based text classification (TC) to match safe approaches identified in existing resources with unsafe scenarios. These safe approaches can serve as initial references and enrich the solution space when performing JHA. Various document modification strategies are applied to existing resources in order to achieve superior TC effectiveness. The end result of this research is a construction safety domain ontology and its underlying knowledge base. A user scenario is also discussed to demonstrate how the ontology supports JHA in practice.     

改进贝叶斯分类的智能短信分类方法

随着移动通信技术的不断发展,手机的普及率在不断上升,而短信作为传统的移动通信服务,长久以来一直在人们的日常生活中占据着极为重要的位置。可以说,短信在一定程度上记录了人们生活的轨迹。但是,现有的短信管理系统仅对短信进行以联系人为特征分类、以时间为顺序显示的简单非智能化的管理,导致了用户手机中各类短信混杂不清,短信的管理效率极低。通过研究短信的特征,分析传统的基于文档频率的特征值提取方法和基于互信息的特征值提取方法的优势与不足,提出了一种适用于短信的基于词频和互信息的特征值提取方法,并结合短信长度实现了一种改进的贝叶斯分类算法。实验证明,算法在进行短信分类时可以得到相当可观的召回率和准确率。     

二维条形码在机关公文管理中的应用

办公自动化系统的发展和应用,对于政府转变职能,提高办公效率起到了举足轻重的作用,但是在实际应用过程中也碰到了很多问题,其中之一就是对公文的管理。传统办公自动化系统的目标是实现办公全过程的＂无纸化＂,即把纸质文件转化为电子文件进行流转。但在目前国情下,要一步实现无纸化办公,还面临很多困难,因此政府机关不得不将纸质文件流和电子文件流并行运行,从而导致需要分发交换的纸质文件和文件的电子信息脱节。通过将条码技术引入到公文管理中,生成含有文件信息的二维条码,从而实现纸质文件和文件的电子信息完全同步。     

归档文件整理规则实施后的思考

《归档文件整理规则》实施后,文书档案管理方式发生了变化,对档案的整理在一定程度上产生了影响,应该在实施过程中总结经验,在探索新方法的过程中加以完善。     

基于Android的网络课程移动学习辅助教学系统设计与应用

在分析Android系统层次架构的基础上,以广州市广播电视大学开放教育楼宇智能化工程专业《数字社区信息化系统工程》课程为例,提出了一种基于Android的网络课程移动学习辅助教学系统,阐述了该系统的分析、设计和实现的全过程.该系统通过开发Android应用程序以支持网络课程的移动学习,为网络课程快速实现移动学习提供了一种新的途径,具有较强的实用和参考价值.