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Comparative Study on the Methodologies of Analyzing Textual Genres and Styles by Means of Multivariate Analysis

多元分析文本体裁与风格分析方法的比较研究

基本信息

批准号：
13610579
负责人：
NAKAMURA Junsaku
金额：
$ 1.47万
依托单位：
The University of Tokushima
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2002
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13610579/
关键词：
Corpus Quantification of Contingency Table Correspondence Analysis Principal Component Analysis BNC World Edition Dickens Manner Adverbs Style ジャンル

项目摘要

Multivariate analysis such as Factor Analysis has long been used in analyzing corpus data. B. Biber (1988 and others) are the typical examples of its use in text typology and seem to have been successful in explaining the differences of registers. However, other multivariate methods like Principal Component Analysis (PCA) and Quantification of Contingency Table (QCT) have also been employed for more or less the same purposes. Burrows (1989 and others) used PCA for stylistic investigations of Jane Austin's novels, and Sigley (1998) used it in proposing a formality index of different text types. One of the investigators of the present research, Tabata (1995 and others), also used it in investigating stylistics of Dickens' texts. These studies also seem to have been successful in their ways of analyzing textual data The same can be said of Nakamura (2002 and others) which made use of QCT in determining the structures of corpora based upon the distributions of various kinds of linguistic f … More eatures or items.Pilot studies by means of QCT concerning the distributions of degree, frequency and manner adverbs across different text categories of the BNC Sampler revealed that manner adverbs behaved differently from the others : the distributions of degree and frequency adverbs were mainly ascribed to the dichotomy of spoken vs. written texts ; imaginative texts played the main role in explaining the behaviors of manner adverbs and the dichotomy of registers turned out to be a secondary factor. In the present research, the behaviors of ?ly manner adverbs across different text categories in the BNC World Edition will be further examined both by PCA and QCT. Column-wise Analysis of PCA turned out to be effective in classifying text domains but the distribution of adverbs are not so effective in separating them into meaningful groups. Row-wise analysis could not extract factors to produce meaningful interpretations. In contrast, QCT provided quite reasonable interpretation for both the distribution of textual domains and that of adverbs with the primary factor being narrative vs. expository style and the secondary factor being informal vs. formal style.Tabata also used Dickens's novels and sketches and conducted several studies : 1) correspondence analysis (ANACOR : same as QCT in principle) of word-class distributions across texts, 2) PCA of 30-60 most common word-types in the dialogue of Dickens's fiction and 3) ANACOR of 1246 types of ' ?ly' adverbs. These studies reveal various aspects of Dickens's texts variation across text categories, variation over time, differentiation of ideolects in fictional discourse due to social dynamics and formal vs. colloquial styles. Further analysis of manner adverbs, the main interest of the present research, revealed that ANACOR generally works better than PCA.In conclusion, in order to choose an appropriate for multivariate analysis, the types of data in question such as types of variable, I.e., quantitative or qualitative, whether raw scores are normalized or not, the number of variables, etc should be taken into consideration. Less

多元分析，如因子分析，长期以来一直被用于分析语料库数据。B。Biber（1988）等人是语域理论在文本类型学中的典型应用，并成功地解释了语域的差异。然而，其他多变量方法，如主成分分析（PCA）和列联表定量（QCT）也已被用于或多或少相同的目的。Burrows（1989）和其他人使用PCA对简·奥斯汀的小说进行文体调查，Sigley（1998）使用它提出了不同文本类型的正式索引。本研究的研究者之一，Tabata（1995和其他人），也用它来研究狄更斯的文本文体。中村（2002）等人也是如此，他们利用QCT来确定语料库的结构，这种结构是基于各种语言特征的分布。 ...更多信息通过QCT对程度副词、频率副词和方式副词在BNC Sampler不同语篇类别中的分布情况进行初步研究，发现方式副词的表现与其他类型不同：程度副词和频率副词的分布主要归因于口语和书面语篇的二分法;想象语篇是解释方式副词行为的主要因素，语域的二分法是次要因素。在本研究中，行为的？在BNC世界版中，不同文本类别的方式副词将由PCA和QCT进一步研究。主成分分析的逐列分析被证明是有效的分类文本域，但副词的分布是不是那么有效地将它们分成有意义的组。逐行分析无法提取因子以产生有意义的解释。相比之下，QCT对语篇域和副词的分布都做出了比较合理的解释，主要因素是叙述性和临时性风格，次要因素是非正式和正式风格。（ANACOR：原则上与QCT相同），（2）狄更斯小说对白中30-60种最常见的词类的主成分分析（PCA），（3）1246种“？ly副词这些研究揭示了狄更斯小说文本在不同文本类别之间的差异、随时间的变化、社会动态和正式与口语风格导致的小说话语中的意识形态差异等各个方面。进一步分析方式副词，本研究的主要兴趣，揭示了ANACOR一般比PCA更好。总之，为了选择一个合适的多变量分析，数据类型的问题，如类型的变量，即，应考虑定量或定性、原始分数是否标准化、变量的数量等。少