Font Size: a A A

Chinese Near-synonymous Degree Adverbs

Posted on:2018-11-18Degree:MasterType:Thesis
Country:ChinaCandidate:H Y XingFull Text:PDF
GTID:2335330518493227Subject:Foreign Linguistics and Applied Linguistics
Abstract/Summary:PDF Full Text Request
Near-synonyms,which address the question of what words are linked with a particular semantic value(Geeraerts et al.1994),have gained considerable attention in the field of Cognitive Linguistics.We can find near-synonymous words of various parts of speech in Chinese.The degree adverbs,as the most frequent adverbs in modern Chinese adverbs,have highly blurry semantics and flexible syntactical functions.Therefore,studies on near-synonymous degree adverbs are worth detailed investigation and play an important role in second language teaching.With the theories of usage-based linguistics,the notion of context-based collocation in Corpus Linguistics,and Construction Grammar in Cognitive Linguistics as the theoretical framework,this study takes the construction [degree adv.+ adj.] as the starting point;we do so implementing collostructional analysis and exploratory multivariate statistical techniques to corpus data.First,we extract concordances with 28 degree adverbs from the Chinese National Corpus and employ Collexeme Analysis,which is used as input for hierarchical cluster analysis for the purpose of finding out semantically motivated near-synonymous degree adverbs,i.e.feichang,shifen,jiqi and jiwei.Next,to amplify the differences among the above four words,we conduct Multiple Distinctive Collexeme Analysis,which is then used as input for correspondence analysis in order to summarize and visualize their distinctions.The findings will not only shed light on lexicography,but also open up new theoretical avenues to language teaching.Methodologically,the present study presents new methods for investigating near-synonyms in a corpus-based quantitative way.
Keywords/Search Tags:near-synonymys, Chinese degree adverbs, collostructional analysis, exploratory multivariate analysis, corpus-based
PDF Full Text Request
Related items