A multi-dimensional analysis of a learner corpus

维数(图论) 风格(视觉艺术) 语言学 计算机科学 名词短语 读写能力 名词 语域(社会语言学) 自然语言处理 人工智能 心理学 数学 艺术 文学类 教育学 哲学 纯数学
作者
Bertus van Rooy,Lize Terblanche
出处
期刊:BRILL eBooks [Brill]
卷期号:: 239-254 被引量:15
标识
DOI:10.1163/9789042025981_014
摘要

The present study reports on a multi-dimensional analysis (Biber, 1988) of the Tswana Learner English (TLE) corpus, together with the Louvain Corpus of Native English Essays (LOCNESS). A new multidimensional model is extracted, since the similarities between nativeness and non-nativeness mask differences between linguistic features to such an extent that it is not possible to come to a complete understanding of such differences using the standard 1988 model. A basic five factor model was extracted. Dimension 1 can be taken to capture advanced literacy, specifically as far as complex noun phrase structure is concerned, with the function of expressing information densely. Dimension 2 can be regarded as an indication of transparency and Dimension 3 captures a range of informal style features. The features that group together as Dimension 4 represent a style of writing that is more nuanced and precise and as a provisional label, we propose contextualisation of information. Dimension 5 can be regarded as the persuasive dimension in student writing, a feature that has been identified as a very important characteristic by Biber and Grabe (1987), and also in our own study of student writing. The most striking differences between the two corpora are on Dimensions 1 and 4. LOCNESS shows more advanced literacy than the TLE, and also contextualises information more extensively than the TLE. On the other dimensions, both corpora contain essays that display the various different styles available, showing that as a register, student writing allows for some internal stylistic variation independent of whether the writers are native or non-native speakers of English. The results confirm the usefulness of the multidimensional model, particularly to the extent that a new model is extracted. Substantial overlap between some of the dimensions in this study and dimensions in other models indicate that multidimensional modals are sensitive to particular kinds of feature groupings, which should be taken as evidence in favour of the general validity of this kind of approach.
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