语言理解问题从认知的角度已有大量的研究,但针对汉语的研究却很少。由于认知实验操作复杂,不容易大规模复制,因此难以量化其结论的普遍性以及对语言事实的覆盖度。该文尝试模拟人补足汉语篇章片段中话题-说明信息的过程,建立广义话题结构认知机模型,并通过认知机对大规模汉语语料进行定量分析,考察汉语标点句的话题认知所需的记忆资源及认知局限性。用作统计特征量的广义话题结构特征有标点句的深度、话题结构内折返度、话题栈深度、话题栈折返度、搁置区使用量。统计数据可从认知行为的视角得到合理解释。该文一方面揭示了说汉语者的话题认知能力的表现和局限性,另一方面又说明了广义话题结构认知机是话题认知的合理模型。
Abstract
Despite the substantial researches on language understanding from cognitive perspective, Chinese language remains a less touched issue. Since cognitive experiments are too complicated to replicate on a large scale, it is difficult to quantify their generalizability and the degree of coverage in all language facts. We construct a Generalized Topic Structure Cognitron (GTSC) by simulating human's cognitive process on complementing the topic-comment information of Chinese Punctuation Clauses (P-clause). With quantitative analysis of large-scale Chinese texts by the way of GTSC, we study the required human memory resources and the cognitive limitation in P-clause understanding. The features adopted in Generalized Topic Structure analysis include the depth of P-clause, the returning degree within topic structure, depth of topic stack, returning degree of topic stack and the number of lay-down area. The statistical results of Generalized Topic Structure produced by GTSC can be explained reasonably from cognitive perspective. This paper reveals the cognitive ability and limitation of Chinese people in topic-comment information processing. In addition, it proves that the GTSC is a reasonable model for cognitive processing of topics in Chinese language.
关键词
广义话题结构 /
认知机 /
认知复杂度 /
标点句 /
话题自足句 /
汉语篇章
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Key words
generalized topic structure /
cognition /
cognitive complexity /
punctuation clause /
topic sufficient sentence /
chinese text
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参考文献
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脚注
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基金
国家自然科学基金(61171129)
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