语言依赖网络视角下中国阿尔茨海默病患者的句法障碍

IF 0.7 2区 文学 0 LANGUAGE & LINGUISTICS
Jianpeng Liu, Junhai Zhao, Xiaohui Bai
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引用次数: 3

摘要

摘要本研究采用依赖网络方法对中国阿尔茨海默病患者的句法障碍进行了研究。依赖树库和依赖网络是从患者群体及其健康同伴的话语中构建的。通过分析两组依赖网络的对比,我们发现1)AD组的平均依赖距离(MDD)比HP组短;AD组和HP组的MDD均远低于中国标准MDD;2) “记住”、“忘记”、“知道”等内容词和“不知道”、“不记得”、“不能说”等动词的否定形式表现出高度重复的不确定否定表达,是AD患者从句谓语的典型表现;3) AD依赖网络中的功能词顶点具有较高的网络参数,如“介数”中心性、贴近度中心性和聚类系数等,表明AD的语法受损,具有更简化的刻板印象特征。这些结果表明,AD组的句法从词性到整个句法结构都受到了损害。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Syntactic Impairments of Chinese Alzheimer’s Disease Patients from a Language Dependency Network Perspective
ABSTRACT This study examined the syntactic impairments of Chinese Alzheimer’s disease patients with a dependency network approach. The dependency treebanks and dependency networks are constructed from the discourses of both the patient group and its healthy peers. By analysing the contrasts in the dependency networks of the two groups, we found that 1) the mean dependency distance (MDD) of the AD group is shorter than that of the HP group; furthermore, the MDDs of both AD and HP groups are far below the standard Chinese MDD; 2) the content words like remember, forget, know, etc. and the negative forms of the verbs like don’t know, can’t remember, can’t say, etc. show highly repetitive uncertain and negative expressions that are typical of the predicates of the clauses of AD patients; 3) the function word vertices in the AD dependency network have distinctive network parameters such as higher ‘betweenness’ centrality, closeness centrality, and clustering coefficients, etc., indicating that the syntax of AD is impaired and features more simplified stereotypes. These results indicate that the syntax of the AD group has been impaired from parts of speech to the whole syntactic structure.
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来源期刊
CiteScore
2.90
自引率
7.10%
发文量
7
期刊介绍: The Journal of Quantitative Linguistics is an international forum for the publication and discussion of research on the quantitative characteristics of language and text in an exact mathematical form. This approach, which is of growing interest, opens up important and exciting theoretical perspectives, as well as solutions for a wide range of practical problems such as machine learning or statistical parsing, by introducing into linguistics the methods and models of advanced scientific disciplines such as the natural sciences, economics, and psychology.
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