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

2016 Fiscal Year Final Research Report

• 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
• 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

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.

Free Research Field

日本語教育学