2006 Fiscal Year Final Research Report Summary
A Syntactic Study of Collocation Using the Corpus
| Project/Area Number |
16520308
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
English linguistics
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| Research Institution | Toyota National College of Technology |
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Principal Investigator |
KAMIYA Masaaki Toyota National College of Technology, General Education, Professor, 一般学科, 教授 (40194980)
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| Co-Investigator(Kenkyū-buntansha) |
TAKAHASHI Kaoru Toyota National College of Technology, General Education, Professor, 一般学科, 教授 (90216705)
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| Project Period (FY) |
2004 – 2006
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| Keywords | Collocation / BNC / Multivariate Analysis / Resultative Construction / Fixed Construction |
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| Research Abstract |
We deal with the idiomatic constructions, such as Wit were not for, it is no use〜ing, and the resultative construction. We focus particularly on the resultative construction.
The resultative construction can be classified into the following three constructions according to the characteristics of the verb
Original Resultative Construction
Derivative Resultative Construction
Intransitive Resultative Construction
We can say the following based on the findings and the research results using the corpus.
Resultative constructions are seen in a lot of languages. But only English, German, and Dutch can make the Derivative Resultative Construction and the Intransitive Resultative Construction. We can say that the resultative construction is a structure to show the feature of the Germanic languages.
As for Multivariate Analysis, in the analysis of EHT3, pragmatically, both features and domains have weights on every dimension simultaneously, and these weights can help us to interpret each dimension when characterising text types by taking into account the location among dimensions and features. It is the dimensions that can characterize text types. At the stage of the interpretation of dimensions, by considering the variations and relationships among features and the domains, we can decide upon dimensions to characterize text types. This technique is called biplot. Actually, PCA employs biplot, so both analyses have an advantage in terms of employing biplot.
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