Web文本挖掘中若干问题的研究
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摘要
随着互联网和电信网的飞速发展,网络文本成为信息的重要载体及不可或缺的主要来源。Web文本挖掘采用数据挖掘、模式识别、信息检索、自然语言处理等学科的知识,解决如何从纷繁复杂的文本信息中获取可理解、易用的知识的问题。本文针对Web文本挖掘中文本分类、短信过滤、信息检索和复杂网络等若干关键问题进行了如下的研究：
(1)多类文本分类。本文针对纠错输出编码法ECOC (Error Correcting Output Code)在解码方面的不足,提出了一种基于支持向量机和概率纠错输出编码的多类文本分类算法。通过构造合适的编码矩阵训练多个两类分类器,并采用Sigmoid函数使其决策函数值概率化。提出两种判别测试文本类别的解码方式：类序列概率计算法和求编码矩阵伪逆法。在标准中英文数据集上的实验结果表明,本文的方法优于ECOC法传统的解码方法及其他经典分类算法。在样本类别分布不均的情况下,该算法仍保持较稳定的准确率。
(2)演进式垃圾短信过滤。针对垃圾短信过滤中存在的内容变化快、用户个性强、训练样本少等问题,本文提出了一种演进式垃圾短信过滤算法和系统。首先提出了基于朴素贝叶斯分类器的演进式基本过滤算法和系统,主要创新点在于灵活的用户反馈方式、自适应学习和进化学习。根据用户使用手机的习惯,提出三种个性化反馈训练样本和类别标签的方式。自适应学习和进化学习的功能分别是更新短信模型中各特征项的权重及特征项本身。为了解决短信训练样本少且精度要求高的问题,提出一种基于中间层映射的垃圾短信过滤算法。实验结果表明,演进式短信过滤方法能够有效接收以数据流传入的短信,并自动更新过滤器。基于中间层映射的过滤算法精度收敛迅速,且在训练样本充足后可与传统分类算法结合使用,继续提高过滤精度。
(3)面向Web实体的搜索。本文以参加的文本检索会议TREC(Text REtrieval Conference)评测的实体追踪(Entity Track)任务为主线,针对网页中的实体提出了一系列挖掘和检索的算法。实体抽取采取了手工辅助自动、规则结合统计的方法,创建了包含多个类型的实体词典。为实体排序提出了文档中心模型DCM(Document-Centered Model)和实体中心模型ECM (Entity-Centered Model),并在此基础上引入语义类别标签,提高检索的精度。另外,基于网页中实体应存在唯一标识的设定,提出了基于规则的主页分配算法。排名第一的评测结果验证了算法的有效性。另一方面,在半结构化的英文维基百科数据集上测试,引入语义类别标签将原有两种模型算法的NDCG指标分别提升了12.1%和25.6%。
(4)基于激活力和亲和度的复杂网络建模与应用。本文以自然语言文本为例,通过词频、共现、距离等统计量模拟生物学和心理学上的词激活效应,计算词激活力WAF (Word Activation Force)。基于WAF计算词的亲和度,建立无向的词网络,研究词的语义相似性在此基础上,将WAF和亲和度用于文本表示、特征选择和文本分类。本算法还可以用来对蛋白质相互作用网络建模,分析蛋白质的关联性除此之外,实体的亲和度还有助于改善实体检索的排序效果。实验结果表明基于激活力和亲和度的复杂网络建模对Web文本挖掘具有重要意义。
With the rapid development of Internet and telecommunication network, web text becomes the important carrier of information and indispensable source. Web text mining depends on the theories in the fields of data mining, pattern recognition, information retrieval, natural language processing, etc. It aims to get comprehensible and easy-to-use knowledge from numerous and complicated texts. This dissertation focuses on several key problems in web text mining, such as text categorization, SMS filtering, information retrieval, complex network, etc.
(1) Multiclass text categorization. This dissertation aims at the lack of Error Correcting Output Code (ECOC) in decoding, and proposes a method of multiclass text categorization based on Support Vector Machine (SVM) and probabilistic ECOC. Several binary classifiers are trained according to appropriate encoding matrix. Values of decision functions are transformed to probabilities by a sigmoid-style function. Two decoding algorithms are introduced for classifying samples. One is calculating the probabilities of each classes, the other is solving the pseudo-inverse of the encoding matrix. Experiments on standard Chinese and English datasets show that the methods are superior to traditional ECOC and other classic algorithms. Moreover, our methods keep stable precision in the condition that samples of each class are not evenly distributed.
(2) Evolutionary SMS filtering. This dissertation proposes a series of algorithms and systems of evolutionary SMS filtering for difficulties of fast updates, personality and lack of training samples. First, a basic evolutionary system is introduced based on Naive Bayes classifier. Its innovations lie in flexible feedback for users, adaptive learning and evolutionary learning. Three types of personalized feedback are put forward according to the uses'habits. Evolutionary learning and adaptive learning are used to update features and their weights. Moreover, this dissertation proposes an interlayer mapping-based SMS filtering algorithm to address the problem in not only high precision but also few training samples. Experimental results show that the proposed method can effectively receive the stream of short messages and update the filter automatically. Interlayer mapping-based filtering algorithm achieves required accuracy with rapid convergence. It can be combined with traditional methods for boosting the performance when samples are enough for training.
(3)Web entity-oriented search. This dissertation proposes a set of algorithms and systems for entity mining and retrieval based on the Entity Track at Text REtrieval Conference (TREC). Entity lexicons including dozens of types for entity extraction are established through semi-automatic, rule-based and statistic-based methods. Document-Centered Model (DCM) and Entity-Centered Model (ECM) are proposed for entity ranking. In addition, semantic category labels are introduced for improving the accuracy. Considering entities in web pages should be identified uniquely, a rule-based algorithm of homepage allocation is presented. Ranking first in official assessment testifies the effectiveness of the proposed methods. Besides, testing on the semi-structured English Wikipedia dataset indicates that semantic category labels improve DCM and ECM by12.1%and25.6%at NDCG, respectively.
(4)Modeling and applications of complex network based on activation force and affinity measure. Taking natural language text as an example, Word Activation Force (WAF) like activation effect in biology and psychology is proposed by merging some statistics, such as word frequency, co-occurrence, distance, etc. Then word affinity measure and undirected network used for studying the semantic similarity between words are generated by WAR On this basis, WAF and word affinity measure are applied to text representation, feature selection and text categorization. These methods are also suitable for PPI (Protein-Protein Interaction) network modeling and protein association analysis. In addition, entity affinity measure contributes to the re-ranking in entity retrieval. Experimental results demonstrate complex network modeling based on activation force and affinity measure is of great significance for web text mining.
(1)多类文本分类。本文针对纠错输出编码法ECOC (Error Correcting Output Code)在解码方面的不足,提出了一种基于支持向量机和概率纠错输出编码的多类文本分类算法。通过构造合适的编码矩阵训练多个两类分类器,并采用Sigmoid函数使其决策函数值概率化。提出两种判别测试文本类别的解码方式：类序列概率计算法和求编码矩阵伪逆法。在标准中英文数据集上的实验结果表明,本文的方法优于ECOC法传统的解码方法及其他经典分类算法。在样本类别分布不均的情况下,该算法仍保持较稳定的准确率。
(2)演进式垃圾短信过滤。针对垃圾短信过滤中存在的内容变化快、用户个性强、训练样本少等问题,本文提出了一种演进式垃圾短信过滤算法和系统。首先提出了基于朴素贝叶斯分类器的演进式基本过滤算法和系统,主要创新点在于灵活的用户反馈方式、自适应学习和进化学习。根据用户使用手机的习惯,提出三种个性化反馈训练样本和类别标签的方式。自适应学习和进化学习的功能分别是更新短信模型中各特征项的权重及特征项本身。为了解决短信训练样本少且精度要求高的问题,提出一种基于中间层映射的垃圾短信过滤算法。实验结果表明,演进式短信过滤方法能够有效接收以数据流传入的短信,并自动更新过滤器。基于中间层映射的过滤算法精度收敛迅速,且在训练样本充足后可与传统分类算法结合使用,继续提高过滤精度。
(3)面向Web实体的搜索。本文以参加的文本检索会议TREC(Text REtrieval Conference)评测的实体追踪(Entity Track)任务为主线,针对网页中的实体提出了一系列挖掘和检索的算法。实体抽取采取了手工辅助自动、规则结合统计的方法,创建了包含多个类型的实体词典。为实体排序提出了文档中心模型DCM(Document-Centered Model)和实体中心模型ECM (Entity-Centered Model),并在此基础上引入语义类别标签,提高检索的精度。另外,基于网页中实体应存在唯一标识的设定,提出了基于规则的主页分配算法。排名第一的评测结果验证了算法的有效性。另一方面,在半结构化的英文维基百科数据集上测试,引入语义类别标签将原有两种模型算法的NDCG指标分别提升了12.1%和25.6%。
(4)基于激活力和亲和度的复杂网络建模与应用。本文以自然语言文本为例,通过词频、共现、距离等统计量模拟生物学和心理学上的词激活效应,计算词激活力WAF (Word Activation Force)。基于WAF计算词的亲和度,建立无向的词网络,研究词的语义相似性在此基础上,将WAF和亲和度用于文本表示、特征选择和文本分类。本算法还可以用来对蛋白质相互作用网络建模,分析蛋白质的关联性除此之外,实体的亲和度还有助于改善实体检索的排序效果。实验结果表明基于激活力和亲和度的复杂网络建模对Web文本挖掘具有重要意义。
With the rapid development of Internet and telecommunication network, web text becomes the important carrier of information and indispensable source. Web text mining depends on the theories in the fields of data mining, pattern recognition, information retrieval, natural language processing, etc. It aims to get comprehensible and easy-to-use knowledge from numerous and complicated texts. This dissertation focuses on several key problems in web text mining, such as text categorization, SMS filtering, information retrieval, complex network, etc.
(1) Multiclass text categorization. This dissertation aims at the lack of Error Correcting Output Code (ECOC) in decoding, and proposes a method of multiclass text categorization based on Support Vector Machine (SVM) and probabilistic ECOC. Several binary classifiers are trained according to appropriate encoding matrix. Values of decision functions are transformed to probabilities by a sigmoid-style function. Two decoding algorithms are introduced for classifying samples. One is calculating the probabilities of each classes, the other is solving the pseudo-inverse of the encoding matrix. Experiments on standard Chinese and English datasets show that the methods are superior to traditional ECOC and other classic algorithms. Moreover, our methods keep stable precision in the condition that samples of each class are not evenly distributed.
(2) Evolutionary SMS filtering. This dissertation proposes a series of algorithms and systems of evolutionary SMS filtering for difficulties of fast updates, personality and lack of training samples. First, a basic evolutionary system is introduced based on Naive Bayes classifier. Its innovations lie in flexible feedback for users, adaptive learning and evolutionary learning. Three types of personalized feedback are put forward according to the uses'habits. Evolutionary learning and adaptive learning are used to update features and their weights. Moreover, this dissertation proposes an interlayer mapping-based SMS filtering algorithm to address the problem in not only high precision but also few training samples. Experimental results show that the proposed method can effectively receive the stream of short messages and update the filter automatically. Interlayer mapping-based filtering algorithm achieves required accuracy with rapid convergence. It can be combined with traditional methods for boosting the performance when samples are enough for training.
(3)Web entity-oriented search. This dissertation proposes a set of algorithms and systems for entity mining and retrieval based on the Entity Track at Text REtrieval Conference (TREC). Entity lexicons including dozens of types for entity extraction are established through semi-automatic, rule-based and statistic-based methods. Document-Centered Model (DCM) and Entity-Centered Model (ECM) are proposed for entity ranking. In addition, semantic category labels are introduced for improving the accuracy. Considering entities in web pages should be identified uniquely, a rule-based algorithm of homepage allocation is presented. Ranking first in official assessment testifies the effectiveness of the proposed methods. Besides, testing on the semi-structured English Wikipedia dataset indicates that semantic category labels improve DCM and ECM by12.1%and25.6%at NDCG, respectively.
(4)Modeling and applications of complex network based on activation force and affinity measure. Taking natural language text as an example, Word Activation Force (WAF) like activation effect in biology and psychology is proposed by merging some statistics, such as word frequency, co-occurrence, distance, etc. Then word affinity measure and undirected network used for studying the semantic similarity between words are generated by WAR On this basis, WAF and word affinity measure are applied to text representation, feature selection and text categorization. These methods are also suitable for PPI (Protein-Protein Interaction) network modeling and protein association analysis. In addition, entity affinity measure contributes to the re-ranking in entity retrieval. Experimental results demonstrate complex network modeling based on activation force and affinity measure is of great significance for web text mining.
引文
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[1]工业和信息化部发布2011年8月通信业运行状况http://www.miit.gov.cn/ n11293472/n11293832/n11293907/n11368223/14179427.html.
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