谈谈术语工作中的几个问题

谈谈术语工作中的几个问题全国术语标准化技术委员会第一分委员会全如瑊ＴｅｒｍｉｎｏｌｏｇｙＷｏｒｋｉｎＣｈｉｎａｉｎｔｈｅＰａｓｔＤｅｃａｄｅ￥ＱｕａｎＲｕｘｉａｎ（Ｔｈｅ１ｓｔＳｕｂｃｏｍｍｉｔｔｅｅ，ＣＮＴＣＴＳ）Ａｂｓｔｒａｃｔ：Ａｂｒｉｅｆｒｅ...     

Using kNN model for automatic text categorization

An investigation is conducted on two well-known similarity-based learning approaches to text categorization: the k-nearest neighbors (kNN) classifier and the Rocchio classifier. After identifying the weakness and strength of each technique, a new classifier called the kNN model-based classifier (kNN Model) is proposed. It combines the strength of both kNN and Rocchio. A text categorization prototype, which implements kNN Model along with kNN and Rocchio, is described. An experimental evaluation of different methods is carried out on two common document corpora: the 20-newsgroup collection and the ModApte version of the Reuters-21578 collection of news stories. The experimental results show that the proposed kNN model-based method outperforms the kNN and Rocchio classifiers, and is therefore a good alternative for kNN and Rocchio in some application areas. This work was partly supported by the European Commission project ICONS, project no. IST-2001-32429.     

Computing with words for text processing: An approach to the text categorization

The use of the computing with words paradigm for the automatic text documents categorization problem is discussed. This specific problem of information retrieval (IR) becomes more and more important, notably in view of a fast proliferation of textual information available on the Internet. The main issues that have to be addressed here are: document representation and classification. The use of fuzzy logic for both problems has already been quite deeply studied though for the latter, i.e., classification, generally not in an IR context. Our approach is based mainly on the classical calculus of linguistically quantified propositions proposed by Zadeh. Moreover, we employ results related to fuzzy (linguistic) queries in IR, notably various interpretations of the weights of query terms. Some preliminary results on widely adopted text corpora are presented.     

Gait recognition using linear time normalization

We present a novel system for gait recognition. Identity recognition and verification are based on the matching of linearly time-normalized gait walking cycles. A novel feature extraction process is also proposed for the transformation of human silhouettes into low-dimensional feature vectors consisting of average pixel distances from the center of the silhouette. By using the best-performing of the proposed methodologies, improvements of 8-20% in recognition and verification performance are seen in comparison to other known methodologies on the “Gait Challenge” database.     

短文本数据的自动分类

本文以比较购物搜索中的商品数据自动分类为应用背景,探讨短文本数据的分类问题,比较了常用的文本分类(Text Categorization)算法的特点,在此基础上提出k-NN与NB相结合的多分类器方案,对于NB算法分类不可信的情况下改用k-NN算法进行再次分类,并充分利用NB的中间结果供k-NN剪枝时作参考。实验数据表明该方法在与NB相近的时间复杂度下可明显地提高短文本分类的正确率和召回率,达到实际应用的要求。     

基于树型结构小波变换的虹膜识别方法

虹膜具有可靠性、唯一性、不可伪造性及不可侵犯性等优点,虹膜识别与其它的人类特征识别相比有更高的安全性.提出一种新的虹膜识别方法,该方法利用树型结构小波变换提取虹膜纹理特征,通过计算虹膜间马氏距离进行匹配,最终实现虹膜识别.实验结果表明,与现有算法相比,该算法识别速度快,提取特征的效果好.     

指纹识别系统中的图像预处理技术

根据指纹的特点,提出了一套有效的指纹识别预处理算法.包括前背景分离、基于空域和频域的图像增强、方向滤波、二值化、细化以及细化去噪处理.由于常用算法在指纹处理中存在缺陷,本文改进了增强算法和细化算法.实验证明,经过本算法处理后,减少了指纹图像中的伪结点和原有特征的丢失,有利于指纹特征提取.     

用YACC实现基于词性标记文法的文本信息隐藏

将词性标记文法语言作自然语言的变换域，分别使用分词与词性标记软件和模拟函数将载体文本与秘密信息都变换到该域；运用字符串匹配技术计算秘密信息句子在载体文本中的位置，位置即为密钥；接收方根据密钥提取文法的句子，再通过语法分析的规约动作读出秘密信息，语法分析程序可利用YACC自动生成。该算法不改变载体文本，能较好地解决Wayner的载体文本生成技术面临的载体文本符合语法但不符合语义的问题。     

基于关键字语法规则的军用文书识别系统设计

军用文书识别是军事信息处理技术的重要组成部分，可作为指挥作业、辅助决策等军事系统的基础工具。该文介绍了利用基于关键字组合式语法规则对文字记述式军用文书识别的一种实现机制和框架，并对软件实现过程中遇到的一些技术问题做了分析和处理。在语法规则设计上，通过附加动作规则，实现文书内容和语义的提取。通过设计系统语法学习功能，实现了语法规则不断充实和完善，文书识别能力会不断得到改进，经验证明系统识别效率和准确率非常高。     

特征序列分析方法在文本分类中的应用

把计算生物学中DNA序列分析的一种方法应用到文本分类中，通过分析文档集所产生的可描述类别内在特征的特征序列，给出了一种文本分类方法SSAM，并在Reuters21578数据集上和其它几种常见分类方法的分类效果进行了比较，实验结果显示SSAM的分类效果优于传统的贝叶斯方法，而且具有较快的分类速度。