语言学的交叉学科研究:语言普遍性、人类认知、大数据

发布时间：2018-01-10 12:18

  本文关键词：语言学的交叉学科研究:语言普遍性、人类认知、大数据　出处：《浙江大学学报(人文社会科学版)》2016年01期 　论文类型：期刊论文

  更多相关文章： 依存距离最小化 语言普遍性 认知科学 大数据

【摘要】：麻省理工学院学者近期发表在国际顶尖期刊《美国科学院院报》上的一项语言学交叉研究利用已经公开发布的依存树库,对37种语言进行了统计分析,指出人类语言存在依存距离最小化这一倾向。此研究被媒体热议,但却存在一些缺陷。依存距离是两个句法相关词之间的线性距离,受工作记忆机制的约束,与句法处理的复杂度密切相关。因此,人类语言具有依存距离最小化的倾向。基于句法标注语料库的依存距离最小化研究表明,大数据研究方法在语言认知研究中具有重要作用。现代语言学具有鲜明的交叉学科色彩,语言研究中不同学科的相互借鉴与融合有助于深入揭示语言系统的运作规律以及语言与认知之间的关系。
[Abstract]:A cross-linguistic study published recently by MIT scholars in the journal Proceedings of the National Academy of Sciences uses a publicly published dependency tree library to analyze 37 languages. It is pointed out that there is a tendency to minimize the dependency distance in human language. This study has been widely discussed in the media, but there are some defects. Dependency distance is the linear distance between two syntactic related words, which is restricted by the working memory mechanism. It is closely related to the complexity of syntactic processing. Therefore, human languages tend to minimize dependency distance. The study of dependency distance minimization based on syntactic tagging corpus shows that. Big data's research method plays an important role in the study of language cognition. Modern linguistics has a distinct interdisciplinary color. The mutual reference and fusion of different subjects in language research is helpful to reveal the rules of language system and the relationship between language and cognition.
【作者单位】： 浙江大学外国语言文化与国际交流学院;
【基金】：国家社会科学基金重大项目(11&ZD188)
【分类号】：H0-05
【正文快照】： [在线优先出版日期]2016-01-06[网络连续型出版物号]CN33-6000/CDependency distance,or,dependency length,is taken as an insightful metric of syntacticcomplexity in the framework of dependency grammar(DG).According to dependency grammar,the syntactic structure，

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