Example-Sentence (ES) Information Abstraction and Meta-Language (ML) Techniques Supporting Effective Use of Useful ES in Analysis of Japanese Language

Research Project

Project/Area Number	26370601
Research Category	Grant-in-Aid for Scientific Research (C)
Allocation Type	Multi-year Fund
Section	一般
Research Field	Japanese language education
Research Institution	Shinshu University
Principal Investigator	SAKAGUCHI Kazuhiro 信州大学, 学術研究院人文科学系, 准教授 (70303485)
Project Period (FY)	2014-04-01 – 2017-03-31
Project Status	Completed (Fiscal Year 2016)
Budget Amount *help	¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000) Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000) Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000) Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Keywords	類義語分析 / 例文分析 / 意味分析 / 日本語分析ストラテジー / 正用文 / 類義語 / テキストマイニング / 映像化ストラテジー
Outline of Final Research Achievements	By text mining, this study analyzes ES analysis and semantic explication of synonymous expressions (SE) in analysis of SE by Japanese native speakers, elucidating Japanese-language analysis techniques which abstract ES descriptions to semantic features. When analysis of SE succeeds, ES expressions tend to be dynamic and concrete. Phrases suggesting classifiable meanings of SE appear, as reflected in semantic explication of SE. However, when analysis of SE fails, language elements in ES analysis and in semantic explication of SE are similar, with many phrases and ML constructs making it difficult to grasp the substance, and many references to language features other than meaning. ES tends to be statically described, but abstraction to semantic features and detailed description are inadequate.