不同话语类型影响词汇多样性和词类分布：基于普通话失语库平台的常人研究

目的:初步构建普通话失语库常人数据集,探讨其中不同话语类型的言语产出。方法:根据国际失语库测试标准,采集30例健康人在不同任务类型中的多媒体话语材料,采用CLAN软件进行转录、编码和校验,并对不同话语类型的词汇多样性及词类分布进行统计分析。结果:命题故事的词汇多样性显著高于组图叙事和单图描述;三种话语类型的词类均以动词产出量最多,其次是名词;不同的话语类型具有不同的词类分布模式。结论:话语类型能影响健康人话语的词汇多样性和词类分布,据此可为言语障碍患者在会话水平的评估和干预提供指导。     

基于普通话失语库的命名性失语和健康人对照话语特征探析

目的：以普通话失语库的健康成人语料为对照,分析命名性失语症患者在3种话语任务中的话语特征。方法：选取命名性失语症患者和健康成人分别为失语组和对照组各12例,2组受试者均进行简易精神状态（MMSE）和西方失语症成套测试（WAB）评估,收集单图描述、组图叙事和命题故事3种任务中的多媒体语料,应用计算机语言分析软件(CLAN)分析总句数、单词型话语均长（WMUL）、形次比移动均值（MATTR）、语速和命题密度5项参数,并对词性分布进行统计分析。结果：失语组MMSE评分和AQ明显低于对照组（P＜0.05）;方差分析显示组别和任务无交互作用,组别主效应中失语组WMUL、MATTR、语速、命题密度4个话语参数均显著低于对照组（均P＜0.05）;任务主效应中,命题故事中的总句数和命题密度产出均高于单图描述和组图叙事(均P＜0.05),且语速高于单图描述(P＜0.05);t检验显示单图描述中失语组在WMLU、MATTR、语速和命题密度上均显著低于对照组(均P＜0.05),组图叙事中失语组在总句数、MATTR和语速上显著低于对照组(均P＜0.05),命题故事中失语组在WMLU、MATTR和语速上显著低于对照组(均P＜0.05);各任务的词类在2组间有不同的分布模式,失语组在组图叙事的名词、动词和副词产出都显著偏少,其单图描述的名词、代词和介词产出显著偏多,其命题故事的代词产出显著偏多而介词相反。结论：命名性失语的语速和词汇多样性在各任务中均低于对照组。基于健康成人对照可分析命名性失语者在不同任务中的词类分布特征,在汉语失语库基础上进行话语分析,可以为失语症评估和治疗提供新视角。     

Corpus-based transitivity biases in individuals with aphasia

Background: This study investigated whether individuals with aphasia (IWA) retain verb biases in expressive language. Verb biases refer to the likelihood that a given verb will occur in different sentence structures. We focused on the likelihood of verbs occurring in transitive and intransitive structures.

Aims: The main goal of this study was to determine whether IWA and controls show similar verb biases or whether IWA show a preference for transitive or intransitive structures that supersedes individual verb biases. We also investigated whether IWA show a preference for intransitively or transitively biased verbs, whether verb biases differ as a function of aphasia type, and how verb bias affects errors in IWA’s speech production.

Methods & Procedures: The current study analysed 236 transcribed interviews of IWA from AphasiaBank. All uses of 54 verbs were coded based on the sentence structure and the presence of errors. We report data from 11 transitively biased and 11 intransitively biased verbs.

Outcomes & Results: IWA’s transitivity biases were indistinguishable from controls’ biases. In addition, IWA produced more intransitively biased verbs than transitively biased verbs overall. In ungrammatical productions, IWA’s error rates were higher in sentence structures that conflicted with verb bias and highest when an intransitively biased verb was attempted in a transitive structure.

Conclusions: These findings indicate that IWA are sensitive to verb bias and verb complexity within expressive language. The effects are consistent with previous literature concerning IWA’s sensitivity to verb bias in receptive language tasks and to verb complexity in verb retrieval tasks.