文本信息处理的若干关键技术研究
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摘要
随着信息时代的到来和Internet的日益普及，文本信息迅速膨胀。Internet上有数十亿的网页，成千上万TB的数据。而且，每天有数十万的网页更新，数百万的新的网页加入，使得Internet上的信息丰富而又复杂。如何有效地组织和管理这些信息，并快速、准确、全面地从中找到用户所需要的信息是当前信息科学领域面临的一大挑战。
文本是最基本、最常见的信息载体。本文以文本信息检索模型为基准，对文本信息处理的几个关键技术包括文本分类、文本聚类和近似查询处理等进行研究。文本分类和文本聚类是对数据进行组织和管理的核心技术。近似查询处理需要快速查询到所需信息，这是解决大规模数据集的一个重要技术。
以下是本文的主要研究内容：
(1)文本信息处理的技术基础。包括文档表示模型、切词、特征选择、文本分类和文本聚类。本文简单介绍了集合模型、代数模型、概率模型和概念模型等四种文档表示模型；分析了中文切词的主要问题和主要方法；具体介绍了文档特征及其选择算法；详细介绍了文本分类和文本聚类，并重点概括了一些重要的文本分类和文本聚类算法。
(2)基于混淆矩阵的层次结构构造。在信息化时代，文档的海量化和复杂化使得对文档进行层次分类成为必要。本文根据描述平面分类器的错误情况的混淆矩阵，提出了两种层次结构构造法。一种是层次聚类法，另一种是混淆类别法。层次聚类法采用合并策略，即：初始时将每个样本看作一类，然后根据它们的相似性或距离逐渐合并，直到形成一个大类为止。混淆类别法根据各类别间容易混淆的概率大于某个阈值t形成混淆类别，从而构造层次结构。对这两种算法均给出了详细的算法。最后通过相关实验对这两种层次结构构造方法进行比较。实验结果表明混淆类别法策略优于层次聚类法。主要是因为层次聚类法认为父类下一层的子类间的混淆关系是对称的，而实际文本并没有体现这一点。
(3)文档流派分类研究。文档流派描述的是文档的风格，而不是文档的内容。文档的流派和文档的主题是正交的。也就是说，相同主题的文档可以有不同的流派，相同流派的文档可以描述不同的主题。流派分类在信息检索、信息过滤、反动信息的拦截和网上舆情调查等方面发挥了越来越重要的作用。为了对文档的正反面进行分类，本文提出了基于特征情感色彩的文档流派分类方法(情感分类)。对于情感分类来说，它和基于主题的分类在分类方法上并没有本质区别。可以认为，基于文档的正反面情感分类就是一个普通的二类分类问题。所以，情感特征的选择和情感倾向判定就尤为重要。因此，本文主要研究情感特征词的选择、情感倾向判定和情感倾向权值计算，并研究了几种典型的方法。最后，在国家自然科学基金(60173027)的支助下，开发了一个情感原型系统，并将情感分类方法同传统文本分类方法和基于语义模式的方法进行比较。结果表明：情感分类方法较差，基于语义模式的方法最好，传统文本分类方法居中。但是情感分类方法不需要人工标注训练样本，不需要针对每个主题构建一个独立的分类器，所以这种方法有较强的通用性，而且分类速度也要快很多。
(4)基于小波变换的近似查询处理。传统的决策支持系统(DSS)在数据查询时希望查询系统能够根据提交的查询提供一个准确的结果，反应时间很长，这是一种典型的“黑盒”模式。但在现今的DSS、在线分析处理(OLAP)、在线聚集等领域，往往不需要一个精准的结果，而对系统的反应速度有很高的要求。这时近似查询就应运而生。小波已经证明了在层次分解(压缩)领域的高效率。小波变换通过将GB／TB级的数据压缩为MB级的数据从而达到近似查询的高速反应要求。本文通过这种压缩机制，在前人提出的选择(Select)、投影(Project)和连接(Join)算法的基础上，提出了合并(Union)、差(Difference)和更新(Update)等操作算法。这些操作都是在小波大纲级上进行的。小波大纲是对源数据的压缩。最后给出了有关实验。实验结果表明，在union和difference操作中，使用小波变换的方法优于任意样本(random sampling)法。并且，当数据的更新不是很大时，对小波系数进行update算法的性能几乎和最优小波系数法相当。
With the coming of information era and the prevalence of Internet gradually, text information expands rapidly. There are billions of webs and thousands upon thousands TB data on internet. Besides, there happen millions of web updates on it everyday. This makes information abundant but tanglesome. It is a big challenge how to organize and manage the information efficiently and query the information which users need quickly, whole and exactly.
Text is a type of basic common information. The paper is based on text information retrieval model, and investigates the vital techniques of text information processing including text categorization, text clustering and approximate query processing. Text categorization and text clustering are two core techniques of organizing and managing text data. And the technique of approximate query processing is applied to query the needed information fast, which is a important technique of solving large scale datasets.
The main investigations on text information processing in the paper are listed as follows:
(1) Technical Basic of Text Information Processing. It includes document model, word-dividing, feature selection, text categorization and text clustering. The paper introduces Set Model, Algebraic Model, Probabilistic Model and Concept Model simply; analyses the main problems and methods in process of Chinese word-diving; introduces document feature and feature selection concretely; describes text categorization and text clustering in detail and generalizes some important typical algorithms of text categorization and text clustering.
(2) Constructions for Hierarchical Structure Based on Confusion Matrix. In information era, documents' large scale and complication make necessity to category them hierarchically. The paper represents two tactics to construct hierarchical structure according to confusion matrix which depicts statistic for a flat classifier's errors probability. One is hierarchical clustering. The other is confusion classification. Hierarchical clustering adopts agglomerative algorithm, that is to say: every sample is regarded as a class in initialization then every two classes is combined to one class according to their comparability or distance until there is only one big class left. The method of confusion classification builds the hierarchical structure according to whether confusion probability between classes is bigger than a certain valve t. And there presents detail algorithms about the two techniques. Finally some experiments are taken on and the comparisons of two technologies' performance for hierarchical categorization are put up. And experiment results show that the performance of confusion classification excels to that of hierarchical clustering and confusion classification can improve the precision and recall of flat document classifier.
(3) Document Genre Classification Based on the Feature Sentiment. Document genre doesn't describe concrete content of a document but style of the document. Document genre intersects with document topic. That is to say, there is difference in
writing style of documents although they belong to the same topic and documents with the same genre can describe different topics. Document genre classification has been becoming more and more important in information retrieval, information filtering, counterchecking of reactive information and investigation of public feelings from internet. In order to category positive or negative documents, the paper represents a categorization technology named sentiment categorization which is based on sentiment of documents feature. For sentiment categorization, there is no difference essentially comparing to categorization based on topic. And it can be regarded as a common two-type document categorization. Thus, it is vital to select sentiment features and determine the feature sentiment orientation. The paper investigates mainly the selection of sentiment features, determination of feature sentiment orientation and computation of feature sentiment weight. And some typical methods are brought forward in the paper. Finally, a prototype system is developed and comparison to traditional text categorization and categorization based on semantic pattern is made. Experiment results show that sentiment categorization is inferior to them and categorization based on semantic is best. But it doesn't need label the training samples and not build a self-governed classifier for each topic. Thus it is more general and the speed of its classification is much rapider than other two methods.
(4) Approximate Query Processing Based on Wavelet Transform. Conventional Decision Support System (DSS) will give an exact answer according to users' query code submitted to query system and it will take a long time to execute the process. This is a typical black box pattern. However, today's DSS applications, OnLine Analytical Processing (OLAP) and online aggregation don't need an exact result but have a high demand for response. Approximate query is a solvent to deal with it. Wavelet has proved high efficiency in hierarchically decomposing. Wavelet transformation can compress GB/TB level of data to MB level. According to this compression mechanism, this paper depicts algorithms such as Union, Difference and Update based on previous works. And these operations are processed in level of wavelet synopsis. Wavelet synopsis is a compression of original data. Finally, some experiments are provided, and its results show that the accuracy of using wavelet is better than that of random sampling to do union and difference operations. And when the update amount of data is not too much, the direct update of wavelet is almost as good as the optimal selected wavelet synopses.
文本是最基本、最常见的信息载体。本文以文本信息检索模型为基准，对文本信息处理的几个关键技术包括文本分类、文本聚类和近似查询处理等进行研究。文本分类和文本聚类是对数据进行组织和管理的核心技术。近似查询处理需要快速查询到所需信息，这是解决大规模数据集的一个重要技术。
以下是本文的主要研究内容：
(1)文本信息处理的技术基础。包括文档表示模型、切词、特征选择、文本分类和文本聚类。本文简单介绍了集合模型、代数模型、概率模型和概念模型等四种文档表示模型；分析了中文切词的主要问题和主要方法；具体介绍了文档特征及其选择算法；详细介绍了文本分类和文本聚类，并重点概括了一些重要的文本分类和文本聚类算法。
(2)基于混淆矩阵的层次结构构造。在信息化时代，文档的海量化和复杂化使得对文档进行层次分类成为必要。本文根据描述平面分类器的错误情况的混淆矩阵，提出了两种层次结构构造法。一种是层次聚类法，另一种是混淆类别法。层次聚类法采用合并策略，即：初始时将每个样本看作一类，然后根据它们的相似性或距离逐渐合并，直到形成一个大类为止。混淆类别法根据各类别间容易混淆的概率大于某个阈值t形成混淆类别，从而构造层次结构。对这两种算法均给出了详细的算法。最后通过相关实验对这两种层次结构构造方法进行比较。实验结果表明混淆类别法策略优于层次聚类法。主要是因为层次聚类法认为父类下一层的子类间的混淆关系是对称的，而实际文本并没有体现这一点。
(3)文档流派分类研究。文档流派描述的是文档的风格，而不是文档的内容。文档的流派和文档的主题是正交的。也就是说，相同主题的文档可以有不同的流派，相同流派的文档可以描述不同的主题。流派分类在信息检索、信息过滤、反动信息的拦截和网上舆情调查等方面发挥了越来越重要的作用。为了对文档的正反面进行分类，本文提出了基于特征情感色彩的文档流派分类方法(情感分类)。对于情感分类来说，它和基于主题的分类在分类方法上并没有本质区别。可以认为，基于文档的正反面情感分类就是一个普通的二类分类问题。所以，情感特征的选择和情感倾向判定就尤为重要。因此，本文主要研究情感特征词的选择、情感倾向判定和情感倾向权值计算，并研究了几种典型的方法。最后，在国家自然科学基金(60173027)的支助下，开发了一个情感原型系统，并将情感分类方法同传统文本分类方法和基于语义模式的方法进行比较。结果表明：情感分类方法较差，基于语义模式的方法最好，传统文本分类方法居中。但是情感分类方法不需要人工标注训练样本，不需要针对每个主题构建一个独立的分类器，所以这种方法有较强的通用性，而且分类速度也要快很多。
(4)基于小波变换的近似查询处理。传统的决策支持系统(DSS)在数据查询时希望查询系统能够根据提交的查询提供一个准确的结果，反应时间很长，这是一种典型的“黑盒”模式。但在现今的DSS、在线分析处理(OLAP)、在线聚集等领域，往往不需要一个精准的结果，而对系统的反应速度有很高的要求。这时近似查询就应运而生。小波已经证明了在层次分解(压缩)领域的高效率。小波变换通过将GB／TB级的数据压缩为MB级的数据从而达到近似查询的高速反应要求。本文通过这种压缩机制，在前人提出的选择(Select)、投影(Project)和连接(Join)算法的基础上，提出了合并(Union)、差(Difference)和更新(Update)等操作算法。这些操作都是在小波大纲级上进行的。小波大纲是对源数据的压缩。最后给出了有关实验。实验结果表明，在union和difference操作中，使用小波变换的方法优于任意样本(random sampling)法。并且，当数据的更新不是很大时，对小波系数进行update算法的性能几乎和最优小波系数法相当。
With the coming of information era and the prevalence of Internet gradually, text information expands rapidly. There are billions of webs and thousands upon thousands TB data on internet. Besides, there happen millions of web updates on it everyday. This makes information abundant but tanglesome. It is a big challenge how to organize and manage the information efficiently and query the information which users need quickly, whole and exactly.
Text is a type of basic common information. The paper is based on text information retrieval model, and investigates the vital techniques of text information processing including text categorization, text clustering and approximate query processing. Text categorization and text clustering are two core techniques of organizing and managing text data. And the technique of approximate query processing is applied to query the needed information fast, which is a important technique of solving large scale datasets.
The main investigations on text information processing in the paper are listed as follows:
(1) Technical Basic of Text Information Processing. It includes document model, word-dividing, feature selection, text categorization and text clustering. The paper introduces Set Model, Algebraic Model, Probabilistic Model and Concept Model simply; analyses the main problems and methods in process of Chinese word-diving; introduces document feature and feature selection concretely; describes text categorization and text clustering in detail and generalizes some important typical algorithms of text categorization and text clustering.
(2) Constructions for Hierarchical Structure Based on Confusion Matrix. In information era, documents' large scale and complication make necessity to category them hierarchically. The paper represents two tactics to construct hierarchical structure according to confusion matrix which depicts statistic for a flat classifier's errors probability. One is hierarchical clustering. The other is confusion classification. Hierarchical clustering adopts agglomerative algorithm, that is to say: every sample is regarded as a class in initialization then every two classes is combined to one class according to their comparability or distance until there is only one big class left. The method of confusion classification builds the hierarchical structure according to whether confusion probability between classes is bigger than a certain valve t. And there presents detail algorithms about the two techniques. Finally some experiments are taken on and the comparisons of two technologies' performance for hierarchical categorization are put up. And experiment results show that the performance of confusion classification excels to that of hierarchical clustering and confusion classification can improve the precision and recall of flat document classifier.
(3) Document Genre Classification Based on the Feature Sentiment. Document genre doesn't describe concrete content of a document but style of the document. Document genre intersects with document topic. That is to say, there is difference in
writing style of documents although they belong to the same topic and documents with the same genre can describe different topics. Document genre classification has been becoming more and more important in information retrieval, information filtering, counterchecking of reactive information and investigation of public feelings from internet. In order to category positive or negative documents, the paper represents a categorization technology named sentiment categorization which is based on sentiment of documents feature. For sentiment categorization, there is no difference essentially comparing to categorization based on topic. And it can be regarded as a common two-type document categorization. Thus, it is vital to select sentiment features and determine the feature sentiment orientation. The paper investigates mainly the selection of sentiment features, determination of feature sentiment orientation and computation of feature sentiment weight. And some typical methods are brought forward in the paper. Finally, a prototype system is developed and comparison to traditional text categorization and categorization based on semantic pattern is made. Experiment results show that sentiment categorization is inferior to them and categorization based on semantic is best. But it doesn't need label the training samples and not build a self-governed classifier for each topic. Thus it is more general and the speed of its classification is much rapider than other two methods.
(4) Approximate Query Processing Based on Wavelet Transform. Conventional Decision Support System (DSS) will give an exact answer according to users' query code submitted to query system and it will take a long time to execute the process. This is a typical black box pattern. However, today's DSS applications, OnLine Analytical Processing (OLAP) and online aggregation don't need an exact result but have a high demand for response. Approximate query is a solvent to deal with it. Wavelet has proved high efficiency in hierarchically decomposing. Wavelet transformation can compress GB/TB level of data to MB level. According to this compression mechanism, this paper depicts algorithms such as Union, Difference and Update based on previous works. And these operations are processed in level of wavelet synopsis. Wavelet synopsis is a compression of original data. Finally, some experiments are provided, and its results show that the accuracy of using wavelet is better than that of random sampling to do union and difference operations. And when the update amount of data is not too much, the direct update of wavelet is almost as good as the optimal selected wavelet synopses.
引文
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