Linguistic landscape is an important field in sociolinguistic research. Eye
tracking technology is a common technology in psychological research. There are
few cases of using eye movement to study linguistic landscape. This paper uses
eye tracking technology to study the actual fixation of the linguistic
landscape and finds that in the two dimensions of fixation time and fixation
times, the fixation of native Chinese speakers to the linguistic landscape is
higher than that of the general landscape. This paper argues that this
phenomenon is due to the higher information density of linguistic landscapes.
At the same time, the article also discusses other possible reasons for this
phenomenon.

通过使用眼动追踪技术研究了语言景观的实际注视，本文发现在注视时间和注视次数这两个维度上，中国本族语言使用者对语言景观的注视明显高于一般景观，尤其是由于语言景观的信息密度较高所引起的。同时，本文还讨论了其他可能的原因。

利用眼动追踪研究中文母语者对语言景观图像的注意力

Using eye tracking to investigate what native Chinese speakers notice  about linguistic landscape images

Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task
and a common application in human daily life. Recently, many data-driven
approaches are proposed for the development of CGEC research. However, there
are two major limitations in the CGEC field: First, the lack of high-quality
annotated training corpora prevents the performance of existing CGEC models
from being significantly improved. Second, the grammatical errors in widely
used test sets are not made by native Chinese speakers, resulting in a
significant gap between the CGEC models and the real application. In this
paper, we propose a linguistic rules-based approach to construct large-scale
CGEC training corpora with automatically generated grammatical errors.
Additionally, we present a challenging CGEC benchmark derived entirely from
errors made by native Chinese speakers in real-world scenarios. Extensive
experiments and detailed analyses not only demonstrate that the training data
constructed by our method effectively improves the performance of CGEC models,
but also reflect that our benchmark is an excellent resource for further
development of the CGEC field.

本文提出了一种基于语言规则的方法来构建自动生成语法错误的大规模中文语法错误纠正训练语料库，并提出了一个挑战性的中文语法错误纠正基准，旨在解决当前缺乏高质量标注的训练语料库和测试集不是由中文母语人士制作的问题。实验结果表明，我们提出的训练数据构建方法有效地提高了中文语法错误纠正模型的性能，并且我们的基准是进一步发展中文语法错误纠正领域的绝佳资源。