Graphically Displaying Text

Abstract

There are many examples of text data bases, including literary corpora and computer source code, in which statistics are associated with each line. A visualization technique for this class of data represents the text lines as thin colored rows within columns. The position, length, and indentation of each row corresponds to that of the text. The color of each row is determined by a statistic associated with each line. The display looks like a miniature picture of the text with the color showing the spatial distribution of the statistic within the text. Using this technique, SeeSoft?, a dynamic graphics software tool, can easily display 50,000 lines of text simultaneously on a high-resolution monitor.     

基于TTS模块的超声波障碍物检澳语音播报系统设计

介绍一种基于超声波传感器和科大讯飞TTS语音模块的超声波障碍物距离检测及语音播报系统。系统首先利用单片机控制超声收发换能器探测目标障碍物信息,再经温度补偿后确定与目标障碍物距离,其后通过LCD及语音模块显示并播报测量结果。文中给出了系统的详细硬件设计方案及主要软件流程图。本系统结构简单,工作稳定,操作便利,反应速度快并具有智能语音播报功能,可用于盲人导航、机动车倒车或特定区域非法侵入检测等领域。     

Using scatterplots to understand and improve probabilistic models for text categorization and retrieval

The two-dimensional representation of documents which allows documents to be represented in a two-dimensional Cartesian plane has proved to be a valid visualization tool for Automated Text Categorization (ATC) for understanding the relationships between categories of textual documents, and to help users to visually audit the classifier and identify suspicious training data. This paper analyzes a specific use of this visualization approach in the case of the Naive Bayes (NB) model for text classification and the Binary Independence Model (BIM) for text retrieval. For text categorization, a reformulation of the equation for the decision of classification has to be written in such a way that each coordinate of a document is the sum of two addends: a variable component P(d|c_i), and a constant component P(c_i), the prior of the category. When plotted in the Cartesian plane according to this formulation, the documents that are constantly shifted along the x-axis and the y-axis can be seen. This effect of shifting is more or less evident according to which NB model, Bernoulli or multinomial, is chosen. For text retrieval, the same reformulation can be applied in the case of the BIM model. The visualization helps to understand the decisions that are taken to order the documents, in particular in the case of relevance feedback.     

A three-stage unsupervised dimension reduction method for text clustering

Dimension reduction is a well-known pre-processing step in the text clustering to remove irrelevant, redundant and noisy features without sacrificing performance of the underlying algorithm. Dimension reduction methods are primarily classified as feature selection (FS) methods and feature extraction (FE) methods. Though FS methods are robust against irrelevant features, they occasionally fail to retain important information present in the original feature space. On the other hand, though FE methods reduce dimensions in the feature space without losing much information, they are significantly affected by the irrelevant features. The one-stage models, FS/FE methods, and the two-stage models, a combination of FS and FE methods proposed in the literature are not sufficient to fulfil all the above mentioned requirements of the dimension reduction. Therefore, we propose three-stage dimension reduction models to remove irrelevant, redundant and noisy features in the original feature space without loss of much valuable information. These models incorporates advantages of the FS and the FE methods to create a low dimension feature subspace. The experiments over three well-known benchmark text datasets of different characteristics show that the proposed three-stage models significantly improve performance of the clustering algorithm as measured by micro F-score, macro F-score, and total execution time.     

手语文本自动分词的设计与实现

手语是我国听障人重要交流之一,手语文本自动分词系统对听障人的政治、文化、生活的发展有着重要意义。研发了手语文本自动分词系统,这是在汉语切分的基础上针对手语特点进行手语切分,而且是利用计算机对文本里面的内容进行自动分词。该系统包括基本的自动分词方法、歧义的处理等基本模块,每一环节互相协助,互相依赖,共同决定该系统的价值、质量和应用水平。     

主观题自动评分算法模型研究

本文提出了基于文本分类的主观题自动评分模型。模型采用文本词性相似度和文本浅层相似度作为分类器的条件属性,在一定程度上提高了文本的语义理解。通过对已有文本的学习,使用考生分数作为分类类别构建决策树分类器。将待测文本输入决策树分类器从而实现答案的分类,即完成自动评分。通过与人工阅卷过程对比,验证了系统是有效可行的,符合人工阅卷的过程。