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Agreement Features and Lubukusu Null-Subject Structures

机译:协议特征和Lubukusu空主题结构

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This paper reports an empirical qualitative study that sought to discuss the treatment of agreement features found in Lubukusu which is a pro-drop language. Its main objective was to interpret the agreement features which permit the missing pronominal subjects in Lubukusu structures. Two hypotheses governed this study: a) Agreement features are uninterpretable in nature; they are only assigned values by the subjects governing them. b) Agreement is interpretable in null subject languages and a pronoun of whichever nature (overt or covert) is therefore redundant. According to this study this prediction is false. Crucial evidence in support of the former hypothesis governing this study comes from Lubukusu structures. It is observed that such constructions can have both overt and phonetically empty subject pronouns which are called pro. These pronouns are formally licensed by virtue of the agreement on the finite verbs or auxiliaries. The study adopted the Government and Binding Theory (GB) with some slight aspects of the most recent developments within the Minimalist Program (MP): The Feature Theory. The study uses the researcher's native speaker intuition to generate appropriate data. Thereafter, native speakers of Lukubusu were chosen as informants to verify the generated data. The data collected was coded into simple sentences, embedded structures and expletives. The results show that the inflectional features found in these structures are uninterpretable and are only valued by the interpretability nature of the overt or covert subjects within the structure at hand.
机译:本文报告了一项经验性的定性研究,旨在探讨如何处理Lubukusu(即亲语言)中的协议特征。其主要目的是解释允许Lubukusu结构中缺少代名词主体的协议特征。本研究支配两个假设:a)协议特征本质上是无法解释的;它们仅由管理它们的主题分配值。 b)协议可以用空主题语言解释,因此,无论是什么性质(公开或秘密)的代名词都是多余的。根据这项研究,这种预测是错误的。支持该研究的先前假设的重要证据来自Lubukusu结构。可以看出,这样的结构既可以有明显的也可以是语音上空的主语代词,称为pro。这些代词根据对有限动词或助词的同意而正式获得许可。该研究采用了政府与约束理论(GB),并结合了极简主义计划(MP)最新发展的一些小方面:特征理论。该研究使用研究者的母语使用者直觉来生成适当的数据。此后,选择了卢布苏语的母语人士作为线人,以验证生成的数据。收集的数据被编码为简单的句子,嵌入的结构和专有名词。结果表明,在这些结构中发现的拐点特征是无法解释的,并且仅由当前结构中公开或隐蔽主体的可解释性来评估。

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