本文提出了一个基于人类词汇联想（Human Word Association）的社交媒体主题检测框架。识别这些媒体中讨论的主题已成为一项关键且重要的挑战。该领域的大多数工作都针对英语，但在波斯语方面也已有不少研究，尤其是用波斯语撰写的微博。此外，现有工作更多关注频繁模式或语义关系的挖掘，而忽视了语言的结构化方法。本文提出了一种使用 HWA（一种人类词汇联想方法）的主题检测框架。该方法采用模仿词汇联想心智能力的概念，还计算用于表示词语之间关联程度的“联想引力”。利用该参数可以生成图。通过对该图进行嵌入并使用聚类方法，可以提取主题。该方法已应用于从 Telegram 收集的波斯语数据集。为评估所提框架的性能，开展了多项实验研究。实验结果表明，该方法比其他主题检测方法效果更好。
英文摘要：
In this paper, we propose a framework to detect topics in social media based on Human Word Association. Identifying topics discussed in these media has become a critical and significant challenge. Most of the work done in this area is in English, but much has been done in the Persian language, especially microblogs written in Persian. Also, the existing works focused more on exploring frequent patterns or semantic relationships and ignored the structural methods of language. In this paper, a topic detection framework using HWA, a method for Human Word Association, is proposed. This method uses the concept of imitation of mental ability for word association. This method also calculates the Associative Gravity Force that shows how words are related. Using this parameter, a graph can be generated. The topics can be extracted by embedding this graph and using clustering methods. This approach has been applied to a Persian language dataset collected from Telegram. Several experimental studies have been performed to evaluate the proposed framework's performance. Experimental results show that this approach works better than other topic detection methods.
PERCEPT: A Corpus for POS Tagging and Analysis of Persian-English Code-Mixing
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（德黑兰大学工程学院电气与计算机工程学院）；School of Engineering Science, College of Engineering, University of Tehran（德黑兰大学工程学院工程科学学院）；Tehran Institute for Advanced Studies, Khatam University（哈塔米大学德黑兰高级研究所）
Probing Association Biases in LLM Moderation Over-Sensitivity
Department of Computer Science, Dartmouth College（达特茅斯大学计算机科学系）；Department of Computer Science and Engineering, The Ohio State University（俄亥俄州立大学计算机科学与工程系）
本文研究LLM在内容审查中因主题关联偏差导致的过灵敏问题，提出Topic Association Analysis方法，发现高级模型在误报中存在更强的主题关联偏移，表明需结合关键词过滤与学习主题关联来缓解。
Proposing Topic Models and Evaluation Frameworks for Analyzing Associations with External Outcomes: An Application to Leadership Analysis Using Large-Scale Corporate Review Data
Accenture Japan Ltd（安永日本有限公司）；Kyoto University Institute for the Future of Human Society（京都大学未来人类社会研究所）；OpenWork Inc.（OpenWork公司）；Integral Corporation（Integral公司）
Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks
Virginia Tech（弗吉尼亚理工大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；University of Washington（华盛顿大学）；Epidemico, Inc.（Epidemico公司）；McGill University（麦吉尔大学）
'Warriors of the Word' -- Deciphering Lyrical Topics in Music and Their Connection to Audio Feature Dimensions Based on a Corpus of Over 100,000 Metal Songs
University of Vienna（维也纳大学）；University of Bamberg（班贝格大学）
Semantics of European poetry is shaped by conservative forces: The relationship between poetic meter and meaning in accentual-syllabic verse
Institute of Polish Language, Polish Academy of Sciences（波兰科学院波兰语言研究所）；University of Tartu（塔尔图大学）；Institute of Czech Literature, Czech Academy of Sciences（捷克科学院捷克文学研究所）；Leiden University（莱顿大学）
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model
King's College London（伦敦国王学院）；University College London（伦敦大学学院）；South London and Maudsley NHS Foundation Trust（南伦敦和莫兹利国民保健基金会信托）；Health Data Research UK London, University College London（英国健康数据研究伦敦中心，伦敦大学学院）；University of Augsburg（奥格斯堡大学）；University of Sussex（萨塞克斯大学）；Amsterdam UMC, Vrije Universiteit（阿姆斯特丹大学医学中心，自由大学）；Parc Sanitari Sant Joan de Déu（圣胡安·德杜健康园区）；Universitat de Barcelona（巴塞罗那大学）；Janssen Research and Development LLC（杨森研发有限责任公司）；Amsterdam University Medical Centre, Vrije Universiteit（阿姆斯特丹大学医学中心，自由大学）；GGZ inGeest（GGZ inGeest精神卫生机构）；Davos Alzheimer’s Collaborative（达沃斯阿尔茨海默病协作组织）
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models
Multimodal Art Projection Research Community（多模态艺术投影研究社区）；University of Manchester（曼彻斯特大学）；National University of Singapore（新加坡国立大学）；Dartmouth College（达特茅斯学院）；Peking University（北京大学）；University of Waterloo（滑铁卢大学）
