In this article, we investigate the structure of Croatian linguistic co-occurrence networks. We examine the change of network structure properties by systematically varying the co-occurrence window sizes, the corpus sizes and removing stopwords. In a co-occurrence window of size $n$ we establish a link between the current word and $n-1$ subsequent words. The results point out that the increase of the co-occurrence window size is followed by a decrease in diameter, average path shortening and expectedly condensing the average clustering coefficient. The same can be noticed for the removal of the stopwords. Finally, since the size of texts is reflected in the network properties, our results suggest that the corpus influence can be reduced by increasing the co-occurrence window size.
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
Tohoku University（东北大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Preferred Networks, Inc.（优选网络公司）；The Institute of Statistical Mathematics（统计数学研究所）
Assessing the alignment between infants' visual and linguistic experience using multimodal language models
Department of Psychology, Stanford University（心理学系，斯坦福大学）；Department of Psychology, University of California, San Diego（心理学系，加州大学圣地亚哥分校）；Department of Psychology, Tsinghua University（心理学系，清华大学）
On the referential capacity of language models: An internalist rejoinder to Mandelkern & Linzen
Norwegian University of Science and Technology（挪威科技大学）；University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Texas Institute for Restorative Neurotechnologies（德克萨斯修复神经技术研究所）
How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework
Department Artificial Intelligence in Biomedical Engineering (AIBE)（人工智能生物医学工程系）；Department of Digital Humanities and Social Studies (DHSS)（数字人文与社会科学系）；University of Birmingham（伯明翰大学）；Chair of AI-supported Therapy Decisions（人工智能支持治疗决策教授职位）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Institute of AI for Health, Helmholtz Zentrum München（健康人工智能研究所，海德堡中心慕尼黑）
DECO-MWE: building a linguistic resource of Korean multiword expressions for feature-based sentiment analysis
DICORA, Department of Linguistics and Cognitive Science, Hankuk University of Foreign Studies, Korea（DICORA，语言学与认知科学系，韩国民法大学，韩国）；Université Paris-Est, LIGM, CNRS, UPEM, ESIEE, ENPC, France（巴黎-est大学，LIGM，法国国家科学研究中心，UPEM，ESIEE，ENPC，法国）
1 Institute of Physics of S\ ao Carlos University of S\ ao Paulo, P. O. Box 369, Postal Code 13560-970 S\ ao Carlos, S\ ao Paulo, Brazil \ 2 Institute of Mathematics；Computer Science University of S\ ao Paulo, P. O. Box 369, Postal Code 13560-970 S\ ao Carlos, S\ ao Paulo, Brazil
TACOMORE: Exploring a replicable prompting protocol for LLM-assisted corpus analysis
Department of Linguistics and Communication, University of Birmingham（伯明翰大学语言学与传播系）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；Institute of Foreign Languages and Cultures, University of Tartu（塔尔图大学外国语言与文化研究所）
Gated Multi-Graph Fusion via Graph Attention Networks for Alzheimer's Disease Detection
School of Future Technology, Tianjin University（天津大学未来技术学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；Huiyan Technology (Tianjin) Co., Ltd（慧眼科技（天津）有限公司）
