外语教学与研究 ›› 2021, Vol. 53 ›› Issue (4): 606-617.

• 研究综述 • 上一篇    下一篇

非宾格假设的跨语言类型研究——四十年发展与新动向

张达球 郭鸿杰   

  1. 上海财经大学
  • 出版日期:2021-07-20 发布日期:2021-07-12
  • 基金资助:
    本文受国家社科基金项目“汉英非宾格性题元关系与句法实现对比研究”(14BYY002)及上海财经大学国家社科基金后续项目“汉语VN词汇化及其生成机制研究——形态-句法界面”(2020110414)的资助。

The Unaccusative Hypothesis in cross-typological syntax: Four decades towards new fields

ZHANG Daqiu & GUO Hongjie   

  1. School of Foreign Studies, Shanghai University of Finance and Economics, Shanghai 200433, China
  • Online:2021-07-20 Published:2021-07-12

摘要: Perlmutter(1978)提出的非宾格假设把非及物动词分为非施格动词和非宾格动词,呈现出跨施格类型和宾格类型的混合句法特征。40 多年来,这一假设在生成语法研究中引起了广泛而深入的讨论。上世纪 80 至 90 年代,对这一现象的跨语言研究多聚焦句法、语义描写和理论诠释,同时也伴有语言习得和加工的研究。进入 21 世纪,句法 -语义界面仍然是研究主流,但认知心理加工、神经语言学和病理语言学的关联研究也对这一假设进行了检验,获得了充分的支持证据。国内相关研究虽与西方同步,但基本还是围绕句法与语义关系的探讨和零散的二语习得研究。近年来,国内学者也开始关注相关结构的心理加工研究,但认知神经和病理语言学领域的相关探索暂付阙如;国内外基于语料库和大数据的人工智能深度学习模式的相关研究更是鲜有涉及。本文拟对不同时期国内外该领域相关研究进行梳理,并就存在的问题及信息技术时代新的研究领域和发展动向提出建议。

关键词: 非宾格性假设, 混合语法, 语言习得与加工, 病理语言学, 机器学习

Abstract: The Unaccusative Hypothesis (Perlmutter 1978) initiated four decades ago within the framework of the Relational Grammar proposed a split of intransitives into two subtypes, the unergative and the unaccusative, which has triggered an extensive exploration in Generative Grammar ever since. The split of intransitives demonstrates by nature mixed properties of a language grammar. The related cross-typological linguistic research abroad in the past four decades falls into three primary fields: syntactic and semantic descriptions and interpretations, psycho-cognitive processing, and neurolinguistic and patholinguistic justifications. But here in China our interest is still primarily limited to the theoretical construal with occasional L2 processing, while neurolinguistic and patholinguistic areas are left untouched. This paper revisits what has been done so far and addresses some controversial issues as well. More importantly, we intend to point out that related research based on the corpus and big data seems to be largely neglected, not to mention machine learning. The paper suggests that it is high time for us to be cross-disciplinary.

中图分类号: 

  • H0-06