论文部分内容阅读
连贯是语篇内不同组成部分之间在意义上的联系,本质上是一个从低到高的连续体。连贯评价研究的核心在于发现与提取与这种意义联系有关的语言特征,即语篇连贯预测因子。本研究基于语篇连贯理论,通过自行编写的计算机程序及Word Net语义知识库,提取与中国英语学习者书面语语篇连贯相关的语言特征,包括基础变量、语义关系变量、连系变量、连结变量以及综合变量等5大类共计41种;之后对提取到的语言特征与人工语篇连贯评分结果作相关分析,并根据二者之间相关关系的显著性来确定语篇连贯预测因子。根据相关分析结果,本研究最终确定了二元单位、连系总数、连结总数等与其学习者书面语语篇连贯性显著相关的21种语言特征,即中国英语学习者书面语语篇连贯预测因子。
Coherence is the sense of the relationship between the different components of the discourse, essentially a continuum from low to high. The core of coherent evaluation research lies in the discovery and extraction of linguistic features related to this meaning connection, namely the coherence predictive factor of discourse. Based on the discourse coherence theory, this study extracts linguistic features related to the coherence of Chinese EFL learners’ written language discourse through computer programs and Word Net semantic knowledge base, including basic variables, semantic variables, conjunctive variables, concatenated variables And 41 kinds of comprehensive variables. After that, the correlation between the extracted language features and the coherence score of artificial discourse was analyzed, and the coherence predictive factor was determined according to the significance of the correlation between the two. According to the results of the correlation analysis, this study finally identifies 21 linguistic features that are significantly related to their learners’ coherence in written discourse, ie, the coherent predictors of written English discourse in Chinese EFL learners.