文本分类中词共现关系的研究及其应用
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摘要
在网络信息时代,文本分类作为大规模文本处理的技术基础,有着广泛的应用前景。随着研究的深入,文本分类技术日趋成熟,开始进入实用阶段,对于文本分类算法本身的研究越来越难以取得突破性创新。在这种情况下,寻找新的研究切入点,从基础性问题入手,解决关键技术,再逐步展开应用于分类器性能的改进,对于文本分类技术研究的发展具有重要的意义。
本文从分析文本类别特征入手,提出了文本分类中词语共现关系研究的课题,从关联和相关两个方面研究了词语共现关系,然后提出了多种将词关联和词相关应用于文本分类的方法,可概括为直接的基于关联或相关的文本分类模型构建以及间接的其它文本分类模型的改进这样两种应用方式。
关联和相关的概念源于数据挖掘中的规则有趣性度量,本文将其应用于文本分类,又采用了统计学中对于相关的一般性解释,从线性和非线性两个方面分析了词间相关性,其中线性相关分析包括了线性方程参数求解和线性相关系数的计算,而非线性相关则主要分析了基于概率的相关性度量指标计算。
对于关联分析在文本分类中的应用,本文从关联文本分类模型和关联特征选择这两个方面进行研究。关联文本分类模型是一种基于规则的文本分类模型,在分类规则挖掘算法上,我们提出了一种应用于长频繁集挖掘的基于变动邻域搜索的遗传算法(VNS-GA)设计,在文档类别判定方法上,我们提出了基于规则匹配长度计算的文档区分算法。在关联特征选择的研究中,我们总结了文档类别区分能力和文档覆盖率这两个特征选择原则,提出了k项频集的并集的选择方法。在Yahoo中文文本数据集上的实验结果表明本文提出的长频繁集挖掘算法能够有效地应用于关联文本分类模型,而运用关联特征选择的朴素贝叶斯文本分类模型也大大提高了分类性能。
对于相关分析在文本分类中的应用,本文所作研究包括线性最小二乘拟合(LLSF)分类、LLSF和朴素贝叶斯组合分类和基于概率相关性分析的改进贝叶斯分类。在Reuters-21578文本集上的实验结果可得以下结论：首先,LLSF分类器的效果不够理想,说明在文本分类中词间线性关系表现较弱,完全基于线性关系假设的分类器可能存在较大偏差；其次,LLSF和朴素贝叶斯组合分类的性能要好于两种分类器单独使用的性能,说明尽管LLSF分类效果较差,但作为一种成熟分类算法仍有其价值；最后,基于概率相关性分析的改进贝叶斯比朴素贝叶斯分类在评测指标上有着全面的明显的提高,说明了本文提出的词集相关度计算方法用于改进贝叶斯分类的有效性。
In the age of network information, text categorization, as a fundamental technology of massive text processing, has wide application prospect. With the research progression, text categorization technology is becoming more and more mature. As text categorization has entered into application stage, it's more difficult to get breakthrough innovation of text categorization algorithms. Under such situations, it's of great significance for the developement of text categorization technology research to find new research entry points, start with fundamentals, solve key technology problems, and apply them into improving performance of classifiers.
By proceeding with analysis on document type features, we brought forward the subject of research on term coexistence relation, and analyzed it in two aspects:association and correlation. Several methods of applying terms association and terms correlation in text classification were presented, which can be generalized in two categories such as direct use of building text classifier based on association or correlation analysis, indirect use of improving text classifier based on other techonologies.
The concepts of association and correlation were derived from data mining as evaluation of rule interestingness. In this paper, we applied them into text classification, and adopted the general definition of correlation in statistics, analyzing term correlation in the methods of linear and nonlinear analysis. Thereinto, analysis on linear correlation includes linear equation parameters solution and linear correlation coefficient calculation, and with regard to nonlinear coreelation analysis, measurement indexes based on probablistic theory were calculated.
As for the applications of association analysis in text categorization, we performed research work in associative text classification and assocative text feature selection. Associative text classification model is based on rules. As to classification rules mining, we proposed a method of genetic algorithm design for mining long frequent itemsets based on variable neighborhood search (VNS-GA), and when it comes to document type classification, we proposed a discriminating algorithm based on rules match length calculation. During the research of text associative feature selection, we summarized two principles of feature selection such as ability of document type identification and document coverage rate, and presented the selection method of combination set of k-frequent sets. Experiments on Yahoo Chinese text data set showed that the long frequent itemset mining algorithm proposed in this paper could be applied effectively into associative text classification, and the proposed method of associative feature selection could improve the classification performance of naive bayes (NB) text classifier.
In this paper, what we studied about the applications of correlation analysis in text categorization included text classifier based on linear least square fit (LLSF), ensemble text classifier combining LLSF and NB classifiers, and improved bayesian classifiers based on probabilistic correlation analysis. Several conclusions from results of experiments on Reuters-21578 corpus may be safely drawn. First, LLSF classifier didn't get good results, that could be explained as weak linear relation among terms, which caused comparetively much deviation for the classifier based on linear relation assumption. Second, that LLSF+NB ensemble text classifier got better performance than any single of them showed that, as a mature algorithm, the value of LLSF classifier still existed despite its not-so-good classifying results. And last, improved bayesian classifier based on probabilistic correlation analysis was proved notably better performance overall than NB classifier by experiments, which verified the effectiveness of the method of term set correlative degree calculation applied into improve NB classification.
本文从分析文本类别特征入手,提出了文本分类中词语共现关系研究的课题,从关联和相关两个方面研究了词语共现关系,然后提出了多种将词关联和词相关应用于文本分类的方法,可概括为直接的基于关联或相关的文本分类模型构建以及间接的其它文本分类模型的改进这样两种应用方式。
关联和相关的概念源于数据挖掘中的规则有趣性度量,本文将其应用于文本分类,又采用了统计学中对于相关的一般性解释,从线性和非线性两个方面分析了词间相关性,其中线性相关分析包括了线性方程参数求解和线性相关系数的计算,而非线性相关则主要分析了基于概率的相关性度量指标计算。
对于关联分析在文本分类中的应用,本文从关联文本分类模型和关联特征选择这两个方面进行研究。关联文本分类模型是一种基于规则的文本分类模型,在分类规则挖掘算法上,我们提出了一种应用于长频繁集挖掘的基于变动邻域搜索的遗传算法(VNS-GA)设计,在文档类别判定方法上,我们提出了基于规则匹配长度计算的文档区分算法。在关联特征选择的研究中,我们总结了文档类别区分能力和文档覆盖率这两个特征选择原则,提出了k项频集的并集的选择方法。在Yahoo中文文本数据集上的实验结果表明本文提出的长频繁集挖掘算法能够有效地应用于关联文本分类模型,而运用关联特征选择的朴素贝叶斯文本分类模型也大大提高了分类性能。
对于相关分析在文本分类中的应用,本文所作研究包括线性最小二乘拟合(LLSF)分类、LLSF和朴素贝叶斯组合分类和基于概率相关性分析的改进贝叶斯分类。在Reuters-21578文本集上的实验结果可得以下结论：首先,LLSF分类器的效果不够理想,说明在文本分类中词间线性关系表现较弱,完全基于线性关系假设的分类器可能存在较大偏差；其次,LLSF和朴素贝叶斯组合分类的性能要好于两种分类器单独使用的性能,说明尽管LLSF分类效果较差,但作为一种成熟分类算法仍有其价值；最后,基于概率相关性分析的改进贝叶斯比朴素贝叶斯分类在评测指标上有着全面的明显的提高,说明了本文提出的词集相关度计算方法用于改进贝叶斯分类的有效性。
In the age of network information, text categorization, as a fundamental technology of massive text processing, has wide application prospect. With the research progression, text categorization technology is becoming more and more mature. As text categorization has entered into application stage, it's more difficult to get breakthrough innovation of text categorization algorithms. Under such situations, it's of great significance for the developement of text categorization technology research to find new research entry points, start with fundamentals, solve key technology problems, and apply them into improving performance of classifiers.
By proceeding with analysis on document type features, we brought forward the subject of research on term coexistence relation, and analyzed it in two aspects:association and correlation. Several methods of applying terms association and terms correlation in text classification were presented, which can be generalized in two categories such as direct use of building text classifier based on association or correlation analysis, indirect use of improving text classifier based on other techonologies.
The concepts of association and correlation were derived from data mining as evaluation of rule interestingness. In this paper, we applied them into text classification, and adopted the general definition of correlation in statistics, analyzing term correlation in the methods of linear and nonlinear analysis. Thereinto, analysis on linear correlation includes linear equation parameters solution and linear correlation coefficient calculation, and with regard to nonlinear coreelation analysis, measurement indexes based on probablistic theory were calculated.
As for the applications of association analysis in text categorization, we performed research work in associative text classification and assocative text feature selection. Associative text classification model is based on rules. As to classification rules mining, we proposed a method of genetic algorithm design for mining long frequent itemsets based on variable neighborhood search (VNS-GA), and when it comes to document type classification, we proposed a discriminating algorithm based on rules match length calculation. During the research of text associative feature selection, we summarized two principles of feature selection such as ability of document type identification and document coverage rate, and presented the selection method of combination set of k-frequent sets. Experiments on Yahoo Chinese text data set showed that the long frequent itemset mining algorithm proposed in this paper could be applied effectively into associative text classification, and the proposed method of associative feature selection could improve the classification performance of naive bayes (NB) text classifier.
In this paper, what we studied about the applications of correlation analysis in text categorization included text classifier based on linear least square fit (LLSF), ensemble text classifier combining LLSF and NB classifiers, and improved bayesian classifiers based on probabilistic correlation analysis. Several conclusions from results of experiments on Reuters-21578 corpus may be safely drawn. First, LLSF classifier didn't get good results, that could be explained as weak linear relation among terms, which caused comparetively much deviation for the classifier based on linear relation assumption. Second, that LLSF+NB ensemble text classifier got better performance than any single of them showed that, as a mature algorithm, the value of LLSF classifier still existed despite its not-so-good classifying results. And last, improved bayesian classifier based on probabilistic correlation analysis was proved notably better performance overall than NB classifier by experiments, which verified the effectiveness of the method of term set correlative degree calculation applied into improve NB classification.
引文
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