基于本体的文本分类模型研究
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摘要
在过去的十几年中，将文本自动地归于事先定义好的类别的技术获得了长足发展，这主要是因为以数字形式存储的文档的数目急剧增长，引起了将它们进行有效组织以便于利用的需求。这一过程主要是用机器学习的方法，在事先构造的训练语料上学习各个类别的特征，自动构建出一个分类器。
传统的文本分类方法都是采用向量空间模型的文本表示方法，用关键词作为特征来构建的。然而，向量空间模型的文本表示方法是基于贝叶斯假设之上的，即认为词与词之间没有语义联系。但是在现实文本中的用词往往是有关联的，比如：同义词、上下位关系等。并且用关键词来表示文本的特征虽然简单直观，但有其固有的局限性，主要有包含的类别信息太少，维数过高从而造成数据稀疏等两个问题。用特征串作为类别特征可以在一定程度上解决第一个问题，但又会进一步加剧数据稀疏问题。对第二个问题的解决方法一般是进行降维，去掉一些对分类结果没有影响或影响很小的特征，用剩余的特征来表示文本。根据结果特征的特点，降维方法可以分为(1)特征提取：结果是原始特征的子集；(2)特征抽取：结果不是原始特征的子集。基于概念的文本分类方法，采用概念作为特征，将特征从词空间映射到概念空间，这样多个同义词就对应一个概念，而一个多义词在不同的语境下会被映射到不同的概念，提高了特征的凝聚度，克服了基于关键词的分类方法的缺陷，提高了分类准确率。
本文的研究工作主要包括以下几个方面：
1．建立了基于本体的文本分类模型。
2．提出基于本体获取概念特征的方法。
3．使用概念空间代替词空间，提出相应的权重与相似度的计算方法，建立概念向量空间模型。
4．讨论了K最邻近方法和支持向量机分类器，并将概念向量空间模型的思想运用于这两种分类器。
5．给出新方法的仿真实验。实验结果表明，基于概念的文本分类与基于关键词的文本分类相比，在查准率、查全率和F1测试值上都占有较大优势。
The automated classification of texts into pre-specified categories has gained a rapid progress in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. Machine learning technologies are used in this process to automatically build a classifier by learning, from a set of previously classified documents, the characteristics of categories.
The vector space model (VSM) is a conventional text classification model that represents documents as vectors in a multidimensional space. When key words are extracted from a document collection, each document is represented as a vector of weighted key words frequencies. In the traditional VSM, the system's relevance judgment is based on the basic assumption that documents are related to each other only if there are shared key words in the documents. However, the difficulty lies in the fact that most key words have multiple meanings on the one hand, and on the other hand, some concepts can be described by more than one key word. In addition, the traditional text categorization use key words occurring in documents to determine the class of the documents, but it have two main flaws: the one is less category information, and the other is high dimensionality which causes data sparse. Phrase can be used to relieve the first problem but: it will aggravate the second one. For the second one, the usual way is using dimensionality reduction (DR) methods which can remove none-effect or less-effect features and the left features are used to represent the text. According to the nature of the result terms, DR has two types: (1) Term Selection: the result terms is a subset of the original terms; (2) Term Extraction: the result terms is not a subset of the original terms. The TC method based on concept is not using key words but concepts to make up characteristic items and considering hyponymy-hyponymy relation between synonymy sets. The approach can keep the text information mostly and solve the two problems at the same time.
The main works of this paper were introduced as follows:
1. We established the text categorization model based on ontology.
2. We proposed a method based on ontology that obtained concepts.
3. The keywords are matched against the attribute terms of the concepts in the given ontology, requiring exact matches. Based on the amount of matching terms for each concept a weight for each concept can be defined. We considered the possible application of the proposed theory on calculating similarity degree of documents, which is the fixed domain. These constructed the concept vector model.
4. We introduced KNN and SVM, and they were implemented for the purpose of the proposed document classification.
We empirically tested the proposed model on documents in order to demonstrate the general applicability of the method. The experimental results show that we can incorporate domain ontology to assist in document classification. For some data sets the concept vector model (CVM) is more effective than the vector space model (VSM) based term. Moreover, the performance comparisons of SVM and KNN based on CVM show that SVM achieves better performance than KNN, and SVM training is thus performed over the reduced training set.
传统的文本分类方法都是采用向量空间模型的文本表示方法，用关键词作为特征来构建的。然而，向量空间模型的文本表示方法是基于贝叶斯假设之上的，即认为词与词之间没有语义联系。但是在现实文本中的用词往往是有关联的，比如：同义词、上下位关系等。并且用关键词来表示文本的特征虽然简单直观，但有其固有的局限性，主要有包含的类别信息太少，维数过高从而造成数据稀疏等两个问题。用特征串作为类别特征可以在一定程度上解决第一个问题，但又会进一步加剧数据稀疏问题。对第二个问题的解决方法一般是进行降维，去掉一些对分类结果没有影响或影响很小的特征，用剩余的特征来表示文本。根据结果特征的特点，降维方法可以分为(1)特征提取：结果是原始特征的子集；(2)特征抽取：结果不是原始特征的子集。基于概念的文本分类方法，采用概念作为特征，将特征从词空间映射到概念空间，这样多个同义词就对应一个概念，而一个多义词在不同的语境下会被映射到不同的概念，提高了特征的凝聚度，克服了基于关键词的分类方法的缺陷，提高了分类准确率。
本文的研究工作主要包括以下几个方面：
1．建立了基于本体的文本分类模型。
2．提出基于本体获取概念特征的方法。
3．使用概念空间代替词空间，提出相应的权重与相似度的计算方法，建立概念向量空间模型。
4．讨论了K最邻近方法和支持向量机分类器，并将概念向量空间模型的思想运用于这两种分类器。
5．给出新方法的仿真实验。实验结果表明，基于概念的文本分类与基于关键词的文本分类相比，在查准率、查全率和F1测试值上都占有较大优势。
The automated classification of texts into pre-specified categories has gained a rapid progress in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. Machine learning technologies are used in this process to automatically build a classifier by learning, from a set of previously classified documents, the characteristics of categories.
The vector space model (VSM) is a conventional text classification model that represents documents as vectors in a multidimensional space. When key words are extracted from a document collection, each document is represented as a vector of weighted key words frequencies. In the traditional VSM, the system's relevance judgment is based on the basic assumption that documents are related to each other only if there are shared key words in the documents. However, the difficulty lies in the fact that most key words have multiple meanings on the one hand, and on the other hand, some concepts can be described by more than one key word. In addition, the traditional text categorization use key words occurring in documents to determine the class of the documents, but it have two main flaws: the one is less category information, and the other is high dimensionality which causes data sparse. Phrase can be used to relieve the first problem but: it will aggravate the second one. For the second one, the usual way is using dimensionality reduction (DR) methods which can remove none-effect or less-effect features and the left features are used to represent the text. According to the nature of the result terms, DR has two types: (1) Term Selection: the result terms is a subset of the original terms; (2) Term Extraction: the result terms is not a subset of the original terms. The TC method based on concept is not using key words but concepts to make up characteristic items and considering hyponymy-hyponymy relation between synonymy sets. The approach can keep the text information mostly and solve the two problems at the same time.
The main works of this paper were introduced as follows:
1. We established the text categorization model based on ontology.
2. We proposed a method based on ontology that obtained concepts.
3. The keywords are matched against the attribute terms of the concepts in the given ontology, requiring exact matches. Based on the amount of matching terms for each concept a weight for each concept can be defined. We considered the possible application of the proposed theory on calculating similarity degree of documents, which is the fixed domain. These constructed the concept vector model.
4. We introduced KNN and SVM, and they were implemented for the purpose of the proposed document classification.
We empirically tested the proposed model on documents in order to demonstrate the general applicability of the method. The experimental results show that we can incorporate domain ontology to assist in document classification. For some data sets the concept vector model (CVM) is more effective than the vector space model (VSM) based term. Moreover, the performance comparisons of SVM and KNN based on CVM show that SVM achieves better performance than KNN, and SVM training is thus performed over the reduced training set.
引文
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[2] F. Sebastiani. Machine Learning in Automated Text Categorization. ACM Computing Surveys, 34(1): 1-47, 2002;
[3] Chang C C, Hector G M, Papcke A. Boolean Query Mapping Across Heterogeneous Information Sources. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(4): 515～521;
[4] G. Salton, A. Wong, C.. S. Yang. A Vector Space Model for Automatic Indexing[J]. Communication of the ACM. 1975, (18) 618-620;
[5] Norbert Fuhr, Chris Buckley. A Probabilistic Learning Approach for Document Indexing. Information Systems, 1991;
[6] 姚天顺，朱靖波，张琍等．自然语言理解——一种让机械懂得人类语言的研究(第二版)．北京：清华大学出版社．2002年10月
[7] Miller G. A, Beckwith, R., Fellbaum C., Gross D., et al.. Introduction to WordNet: An On-Line Lexical Database. In Five Papers on WordNet, CSL Report, Cognitive Science Laboratory, Princeton University, 1993
[8] 于江生，俞士汶．中文概念词典的结构．中文信息学报，2002，Vol．16，No．4．12～20
[9] 黄曾阳．HNC理论概要．中文信息学报，1997，Vol．11，No．4，11～20
[10] 何婷婷．基于HNC理论的一种词汇岐义消解规则．华中师范大学学报(自然科学版)．2002，Vol．36，No．1，30-34
[11] 董振东，董强．知网．计算语言学文集，北京：清华大学出版社，1999．10
[12] Li Sujian, Zhang Jian, Huang Xiong, et al. Semantic Computation in a Chinese Question-Answering System. Journal of Computer Science and Technology, 2002, Vol. 17 No. 6, 933～939
[13] Deerwester S., Dumais S. T., Fumas G. W., Landaure T. K., and Harshman, R.. Indexing by Latent Semantics Analysis, Journal of the American Society for Information Science, 1990, Vol. 41, No.6, 391-407
[14] Dumais S., Furnas G., Landauer T., Scott D., et al. Using Latent Semantic Analysis to Improve Access to Textual Information. Proceedings of Computer Human Interaction, 1988. P281-285
[15] 邓志鸿，唐世渭，张铭等．Ontology研究综述．北京大学学报(自然科学版)，2002，Vol．38，No．5，730-737
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