基于内容相关度计算的文本结构分析方法研究
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摘要
文本结构可分为物理和逻辑结构两种形式,文本的物理结构是指组成文本的基本要素(如标题、段落、句子、词汇和标点符号等)在文本中的实际位置所决定的结构,可以用向量空间模型予以表示;文本的逻辑结构是指组成文章思想内容的主题、层次、段落、句子、主题词在概念意义上所形成的逻辑关系,通常用树或图予以表示。文本结构自动分析就是期望计算机能够自动将一个文本划分成互不相交的若干文本单元,或者从语义上将文本解析成为一棵层次结构树,以便获得文章本来的逻辑结构。
文本结构分析对于实现文本理解和文本推理具有重要意义,只有从宏观上把握文章的逻辑结构,才能更合理的从全局的视角理解文章的主题及中心思想;同时,文本结构分析结果对于文本自动摘要、基于篇章段落的信息检索、话题检测与跟踪等自然语言处理任务也具有重要作用和影响。然而,文本结构通常需要在理解上下文内容的基础上才能获得,而对语言的理解又超出了目前计算机的能力和水平,因此,要使计算机在没有理解上下文内容的前提下,准确的分析出文本的组织结构,是一个非常困难的问题。
本文根据文本篇章组织结构理论以及文本组织结构特点,将文本结构分析转换成为线性结构分析或者层次结构分析任务。据此,首先通过研究词语间语义相关度计算方法、句子间语义关系识别和句子间语义相关度计算方法,进行文本上下文内容相关性分析和相关度计算,并以此为基础,研究对文本进行线性结构分析或者层次结构分析的关键技术和方法。
具体来说,本文的创新性工作主要体现在以下几个方面:
(1)对文本结构进行抽象描述。将文本中的“句子”、“标题”、“自然段”、“文章”、“主题/子主题”等语言学概念加以形式标记;提出了“基本论证结构”、“递归论证结构”、“文本结构树”、“文本主题单元”等层次结构概念及表示方法,以便对文本组织结构模式进行抽象描述;对“主题的级”、“主题结点粒度”进行了定量化描述和计算,以便刻画文本结构树中主题结点对内容的涵盖能力。
(2)研究了词语间语义相关关系及相关度计算方法。在分析词语间相关度和相似度概念关系的基础上,提出了词语间语义广义相关度的概念及其相应的计算方法:首先从外延逻辑思想出发,提出了一种基于语料的、通过构建词语语义关系二分图的方法,来计算词语狭义相关度;同时,以汉语概念内涵逻辑模型思想为基础,提出了一种基于词典内涵释义及释义项展开的词语语义相关度计算方法,其计算结果强调的是词语在内涵概念上的关联关系;然后,将两种方法计算的相关度结果进行融合,得到词语语义广义相关度。通过标准的M&C中文版测试数据集评测结果表明,融合得到的广义相关度汲取了外延逻辑刻画实体分类的优势和内涵逻辑刻画汉语凸显实体内涵属性特征的优势,取长补短、优势互补,其计算结果更接近人的认知和判断。
(3)研究了语篇上下文句子之间语义关系及相关度计算方法。首先,根据语言学界总结的句际语义关系和它对应的词语形式标记,提出了一种机器自动识别上下文句际语义关系的方法(定性方法),包括词语形式模板的获取、模板冲突消解的方法以及句际语义关系识别算法,并用实验验证了该方法的有效性和识别效果;其次,提出了一种基于词语广义相关度的句子间相关度计算方法(定量方法),实验表明,本文提出的句子间相关度计算结果比句子间相似度计算结果更接近人的理解和判断。
(4)根据词语广义相关度计算方法、句际语义关系分析与相关度计算方法,研究了文本线性结构分析中的相关问题,提出了一种基于内容相关性分析的文本分割方法,实验表明,本文提出的方法在文本分割性能上要好于经典的TextTiling算法,而且也好于现有文献报道的面向中文的文本分割算法的性能。
(5)研究了文本层次结构分析的相关问题,并假定同一类型的文本应该具有相同或相似的组织结构模式。据此,提出了一种基于Na?ve Bayes模型的文本层次结构分析方法,即用Na?ve Bayes模型从训练文本中学习文本的组织结构模式,再根据获取得到的文本组织结构模式,对待分析的同类型文本,按照自底向上的方式,递归的向上归并,直到生成只包含一个根结点的文本结构树。同时,提出了一种基于生物序列比对算法的文本结构分析方法,从训练文本中学习文本组织结构模式,以便进行文本组织结构分析。实验结果表明,上述两种方法都取得了一定的效果,从目前的测试数据集上看,前者要比后者具有更好的性能。
Text structure can be considered to have both the Physical and Logical structure. The Physical structure of a text is a structure determined by the actual location of the basic components of the text (such as titles, paragraphs, sentences, vocabularies, punctuations, etc.), and Vector Space Model can be used to denote that structure. The Logical structure of a text is a logical relation or logical structure built by subjects, levels, paragraphs, sentences and keywords, which together, based on the concept meaning, reflect the topic or clou of the text, usually expressed by a tree-diagram or a graph. The automatic analysis of text structure is to use the computer to divide a text into a number of disjoint text units (semantic paragraphs), or to parse it into a hierarchical tree based on meaning, so that people can obtain the original logical relation or logical structure of the corresponding text.
The automatic analysis of text structure is a very significant step to achieve the automatic text understanding. Since only by holding the logical organizational structure of the article in a macro level, the topic or clou of the article can be more easily understood from the overall perspective. At the same time, the results of the text structure analysis have an important influence on many other natural language processing tasks, such as automatic text summarization, information retrieval, topic detection and tracking, etc. However, the understanding of a text, which is beyond the capability of computers, is the basis for text structure analysis. Therefore, it is a tough job for computers to analyze the logical structure of a text as accurately as possible without the understanding of a context.
Based on the theory of text organizational structure and the characteristics of text structure, this paper divides the text structure analysis into two tasks: linear structural analysis and hierarchical structural analysis. Hereby, we firstly researched and proposed some approaches to calculate the degrees of semantic relevancy between words in Chinese, to recognise the semantic relation between sentences in a context, and to calculate the degrees of semantic relevancy between sentences, for analyzing the semantic relevancy of the context and calculating the relevancy degrees of the context. Then, based on the relevancy analyzing and calculating of the context content, we in-depth study on the theories and methods concerning linear structural analysis and hierarchical structural analysis. To be specific, the present paper mainly contributes:
(1) To the abstractive description of a text structure. The concepts of‘sentence’,‘title’,‘paragraph’,‘article’,‘topic or sub-topic’, etc. are described formally; new concepts like‘basic argument structure’,‘recursive argument structure’,‘text-structure tree’,‘text-topic units’etc. are proposed and described formally. At the same time, a method to quantitatively describe or calculate‘the level of a topic’and‘the granularity of a topic’is presented. All of these serve for the premise or basis to carry out structure analysis.
(2) To the semantic relevancy relation and relevancy degree between words. By analyzing the relevancy degree and similarity between between words, we propose the concept of‘broad-sense relevancy degree’of word meanings. For calculating the broad-sense relevancy degree between words, we first propose a corpus-based method to calculate the semantic relevancy degree between words through constructing bipartite graph of lexical semantic relation, which is also known as narrow-sense relevancy degree. Secondly, based on the idea of Concept Intersional Logical Model of the Chinese Language, we propose a method of calculating semantic relevancy degree between words in light of the definitions of a lexical item or its sub-item in a dictionary. The results of the calculation stress more on similarity or relevancy between words in their conceptual meanings. Finally, we combine the above two results to form a broad-sense relevancy degree. Tested by the data in the Standard M&C Chinese Version, the results show that the above first and second approaches can complement each other and the combination of which can achieve the result of broad-sense relevancy degree, which is close to what achieved by man’s cognition or judgments.
(3) To the semantic relevancy relation between sentences in a context. First, according to the inter-sentence semantic relationship and its corresponding word-form tags summarized by the specialists in linguistics, we propose an automatic recognition (qualitative) method to recognize the semantic relation between sentences in a context, including the approach to obtain the templates of word-form tags, the approach to resolve the conflict between templates, and the algorithm to recognize the inter-sentence semantic relations. The method of automatic analysis is then tested for its validity and effectiveness. Second, we propose a calculating (quantitative) method based on the generalized semantic relevancy between words to calculate the relevancy degree between sentences. The tests show that the results of the relevancy degree calculation are closer to the man’s judgment than the existing method of similarity calculation that calculates similarity between sentences
(4) To the linear structural analysis of the discourse and its related issues. Based on the above method to calculate the broad-sense relevancy degree between words and to calculate or analyze the semantic relevancy between sentences, we carry out the linear structural analysis of texts and study its related issues, and then presented a text linear segmentation method based on the content relevancy analysis in the context. Tests show that our method is better in segmenting texts than the classic method of TextTiling algorithm, and also better than the existing text segmentation algorithm already reported for Chinese texts.
(5) To the hierarchical structural analysis and its related issues by confirming the idea those texts of the same type should have the same or similar structural mode. Accordingly, we first propose a text hierarchical structural analysis method based on Na?ve Bayes model, namely, to learn text organizational structural mode from the training corpus by using Na?ve Bayes model, and then recursively merge the nodes upward until a tree of text structure with a root node is generated. Moreover, we propose a text hierarchical structural analysis method based on the bio-sequence alignment algorithm. That is, by using the sequence alignment algorithm, it finds the most similar text in text structure from the training corpus as the test text, and acquires its text structural mode. Thus the structure of the test text can be automatically analyzed in the light of structural mode. The test results show that the above two methods work the same. But from the current test data set, the former has better performance than the latter.
文本结构分析对于实现文本理解和文本推理具有重要意义,只有从宏观上把握文章的逻辑结构,才能更合理的从全局的视角理解文章的主题及中心思想;同时,文本结构分析结果对于文本自动摘要、基于篇章段落的信息检索、话题检测与跟踪等自然语言处理任务也具有重要作用和影响。然而,文本结构通常需要在理解上下文内容的基础上才能获得,而对语言的理解又超出了目前计算机的能力和水平,因此,要使计算机在没有理解上下文内容的前提下,准确的分析出文本的组织结构,是一个非常困难的问题。
本文根据文本篇章组织结构理论以及文本组织结构特点,将文本结构分析转换成为线性结构分析或者层次结构分析任务。据此,首先通过研究词语间语义相关度计算方法、句子间语义关系识别和句子间语义相关度计算方法,进行文本上下文内容相关性分析和相关度计算,并以此为基础,研究对文本进行线性结构分析或者层次结构分析的关键技术和方法。
具体来说,本文的创新性工作主要体现在以下几个方面:
(1)对文本结构进行抽象描述。将文本中的“句子”、“标题”、“自然段”、“文章”、“主题/子主题”等语言学概念加以形式标记;提出了“基本论证结构”、“递归论证结构”、“文本结构树”、“文本主题单元”等层次结构概念及表示方法,以便对文本组织结构模式进行抽象描述;对“主题的级”、“主题结点粒度”进行了定量化描述和计算,以便刻画文本结构树中主题结点对内容的涵盖能力。
(2)研究了词语间语义相关关系及相关度计算方法。在分析词语间相关度和相似度概念关系的基础上,提出了词语间语义广义相关度的概念及其相应的计算方法:首先从外延逻辑思想出发,提出了一种基于语料的、通过构建词语语义关系二分图的方法,来计算词语狭义相关度;同时,以汉语概念内涵逻辑模型思想为基础,提出了一种基于词典内涵释义及释义项展开的词语语义相关度计算方法,其计算结果强调的是词语在内涵概念上的关联关系;然后,将两种方法计算的相关度结果进行融合,得到词语语义广义相关度。通过标准的M&C中文版测试数据集评测结果表明,融合得到的广义相关度汲取了外延逻辑刻画实体分类的优势和内涵逻辑刻画汉语凸显实体内涵属性特征的优势,取长补短、优势互补,其计算结果更接近人的认知和判断。
(3)研究了语篇上下文句子之间语义关系及相关度计算方法。首先,根据语言学界总结的句际语义关系和它对应的词语形式标记,提出了一种机器自动识别上下文句际语义关系的方法(定性方法),包括词语形式模板的获取、模板冲突消解的方法以及句际语义关系识别算法,并用实验验证了该方法的有效性和识别效果;其次,提出了一种基于词语广义相关度的句子间相关度计算方法(定量方法),实验表明,本文提出的句子间相关度计算结果比句子间相似度计算结果更接近人的理解和判断。
(4)根据词语广义相关度计算方法、句际语义关系分析与相关度计算方法,研究了文本线性结构分析中的相关问题,提出了一种基于内容相关性分析的文本分割方法,实验表明,本文提出的方法在文本分割性能上要好于经典的TextTiling算法,而且也好于现有文献报道的面向中文的文本分割算法的性能。
(5)研究了文本层次结构分析的相关问题,并假定同一类型的文本应该具有相同或相似的组织结构模式。据此,提出了一种基于Na?ve Bayes模型的文本层次结构分析方法,即用Na?ve Bayes模型从训练文本中学习文本的组织结构模式,再根据获取得到的文本组织结构模式,对待分析的同类型文本,按照自底向上的方式,递归的向上归并,直到生成只包含一个根结点的文本结构树。同时,提出了一种基于生物序列比对算法的文本结构分析方法,从训练文本中学习文本组织结构模式,以便进行文本组织结构分析。实验结果表明,上述两种方法都取得了一定的效果,从目前的测试数据集上看,前者要比后者具有更好的性能。
Text structure can be considered to have both the Physical and Logical structure. The Physical structure of a text is a structure determined by the actual location of the basic components of the text (such as titles, paragraphs, sentences, vocabularies, punctuations, etc.), and Vector Space Model can be used to denote that structure. The Logical structure of a text is a logical relation or logical structure built by subjects, levels, paragraphs, sentences and keywords, which together, based on the concept meaning, reflect the topic or clou of the text, usually expressed by a tree-diagram or a graph. The automatic analysis of text structure is to use the computer to divide a text into a number of disjoint text units (semantic paragraphs), or to parse it into a hierarchical tree based on meaning, so that people can obtain the original logical relation or logical structure of the corresponding text.
The automatic analysis of text structure is a very significant step to achieve the automatic text understanding. Since only by holding the logical organizational structure of the article in a macro level, the topic or clou of the article can be more easily understood from the overall perspective. At the same time, the results of the text structure analysis have an important influence on many other natural language processing tasks, such as automatic text summarization, information retrieval, topic detection and tracking, etc. However, the understanding of a text, which is beyond the capability of computers, is the basis for text structure analysis. Therefore, it is a tough job for computers to analyze the logical structure of a text as accurately as possible without the understanding of a context.
Based on the theory of text organizational structure and the characteristics of text structure, this paper divides the text structure analysis into two tasks: linear structural analysis and hierarchical structural analysis. Hereby, we firstly researched and proposed some approaches to calculate the degrees of semantic relevancy between words in Chinese, to recognise the semantic relation between sentences in a context, and to calculate the degrees of semantic relevancy between sentences, for analyzing the semantic relevancy of the context and calculating the relevancy degrees of the context. Then, based on the relevancy analyzing and calculating of the context content, we in-depth study on the theories and methods concerning linear structural analysis and hierarchical structural analysis. To be specific, the present paper mainly contributes:
(1) To the abstractive description of a text structure. The concepts of‘sentence’,‘title’,‘paragraph’,‘article’,‘topic or sub-topic’, etc. are described formally; new concepts like‘basic argument structure’,‘recursive argument structure’,‘text-structure tree’,‘text-topic units’etc. are proposed and described formally. At the same time, a method to quantitatively describe or calculate‘the level of a topic’and‘the granularity of a topic’is presented. All of these serve for the premise or basis to carry out structure analysis.
(2) To the semantic relevancy relation and relevancy degree between words. By analyzing the relevancy degree and similarity between between words, we propose the concept of‘broad-sense relevancy degree’of word meanings. For calculating the broad-sense relevancy degree between words, we first propose a corpus-based method to calculate the semantic relevancy degree between words through constructing bipartite graph of lexical semantic relation, which is also known as narrow-sense relevancy degree. Secondly, based on the idea of Concept Intersional Logical Model of the Chinese Language, we propose a method of calculating semantic relevancy degree between words in light of the definitions of a lexical item or its sub-item in a dictionary. The results of the calculation stress more on similarity or relevancy between words in their conceptual meanings. Finally, we combine the above two results to form a broad-sense relevancy degree. Tested by the data in the Standard M&C Chinese Version, the results show that the above first and second approaches can complement each other and the combination of which can achieve the result of broad-sense relevancy degree, which is close to what achieved by man’s cognition or judgments.
(3) To the semantic relevancy relation between sentences in a context. First, according to the inter-sentence semantic relationship and its corresponding word-form tags summarized by the specialists in linguistics, we propose an automatic recognition (qualitative) method to recognize the semantic relation between sentences in a context, including the approach to obtain the templates of word-form tags, the approach to resolve the conflict between templates, and the algorithm to recognize the inter-sentence semantic relations. The method of automatic analysis is then tested for its validity and effectiveness. Second, we propose a calculating (quantitative) method based on the generalized semantic relevancy between words to calculate the relevancy degree between sentences. The tests show that the results of the relevancy degree calculation are closer to the man’s judgment than the existing method of similarity calculation that calculates similarity between sentences
(4) To the linear structural analysis of the discourse and its related issues. Based on the above method to calculate the broad-sense relevancy degree between words and to calculate or analyze the semantic relevancy between sentences, we carry out the linear structural analysis of texts and study its related issues, and then presented a text linear segmentation method based on the content relevancy analysis in the context. Tests show that our method is better in segmenting texts than the classic method of TextTiling algorithm, and also better than the existing text segmentation algorithm already reported for Chinese texts.
(5) To the hierarchical structural analysis and its related issues by confirming the idea those texts of the same type should have the same or similar structural mode. Accordingly, we first propose a text hierarchical structural analysis method based on Na?ve Bayes model, namely, to learn text organizational structural mode from the training corpus by using Na?ve Bayes model, and then recursively merge the nodes upward until a tree of text structure with a root node is generated. Moreover, we propose a text hierarchical structural analysis method based on the bio-sequence alignment algorithm. That is, by using the sequence alignment algorithm, it finds the most similar text in text structure from the training corpus as the test text, and acquires its text structural mode. Thus the structure of the test text can be automatically analyzed in the light of structural mode. The test results show that the above two methods work the same. But from the current test data set, the former has better performance than the latter.
引文
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