Research Trends and Developments in Chinese Linguistics over the Past Decade (2016–2025): A Codex-Based Bibliometric Analysis of Peking University Core Journals in Linguistics
Aug 2026· Korea Association of Chinese Linguistics· 0 citations
Abstract
This study analyzes 8,181 papers on Chinese linguistics published in Peking University Core Linguistics Journals between 2016 and 2025 through a Codex-based bibliometric approach, examining the research productivity, scholarly community, knowledge structure, and research frontiers of Chinese linguistics over the past decade. The results show that Chinese linguistics maintained a stable level of research output with little annual fluctuation, while research achievements were concentrated in a small number of core journals and among leading research groups. In terms of knowledge structure, studies on Modern Chinese, historical linguistics, and Chinese philology constituted the core research domains, with grammaticalization, semantic change, and constructional studies identified as the major research themes. In addition, artificial intelligence, large language models, International Chinese Language Education, international communication, and translation strategies emerged as recent major research topics and burst keywords. By providing a comprehensive overview of the knowledge structure and research frontiers of Chinese linguistics over the past decade, this study offers a useful reference for understanding recent research trends and identifying future research directions.
Abstract Earlier research on syntactic complexity has predominantly focused on English as a second language. However, recent years have witnessed a growing interest in the syntactic complexity of Chinese as a second language (L2). Against this backdrop, this paper reviews the current state of research on L2 Chinese syn...
Ying-Ying Liu, Xiao-Fei Lu· Chinese Journal of Applied L...· 0 citations
A bibliometric and topic-based analysis of 7,120 Arabic NLP papers published between 1960 and 2026, sourced from five platforms plus an additional targeted OpenAlex subset, and offers recommendations to prioritize under-resourced dialects and develop culturally aligned benchmarks.
Our study investigated stance markers used in Materials Science research article abstracts by native Chinese-speaking (Chinese L1) and native English-speaking (English L1) researchers, adopting the widely recognized interactional metadiscourse framework. We constructed two comparable corpora, each comprising 501 abstra...
With the rapid development of digital humanities, traditional research on modern Chinese literature is constrained by limited samples and subjective interpretation, while text mining provides a quantitative and systematic analytical approach. This study takes 60 classic works of modern Chinese literature across three h...
Introduction. The article deals with studies of set expressions in contemporary Chinese linguistics. Special attention is paid to the formation of the terminological system of Chinese paremiology, the description of the main types of set expressions, and the problem of correlating Chinese and Russian scholarly traditio...
M. Bredis, Meng-Lin Gao, O. Lazareva et al.· The Oriental studies· 0 citations
Drawing on genre analysis and corpus-based translation studies, this study examines rhetorical hype in Chinese government White Papers published between 2020 and 2023 and their official English translations. Rhetorical hype is defined as evaluative and promotional language that heightens the perceived importance, n...
Luo-Jia Wang· Frontiers in Communication· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.