Contextual-Lexicon Approach for Abusive Language Detection
Institute of Mathematical and Computer Sciences, University of São Paulo（圣保罗大学数学与计算机科学学院）；Ribeirão Preto Medical School, University of São Paulo（圣保罗大学里贝朗普雷图医学院）；Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）
Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social media. Our approach embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to any other language. The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.
ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting
College of Computing, Mohammed VI Polytechnic University（穆罕默德六世理工大学计算学院）；Deep Learning & Natural Language Processing Group, The University of British Columbia（不列颠哥伦比亚大学深度学习与自然语言处理组）；Department of Natural Language Processing & Department of Machine Learning, MBZUAI（穆罕默德·本·扎耶德人工智能大学自然语言处理系与机器学习系）；Department of Electrical & Computer Engineering, University of Sherbrooke（舍布鲁克大学电气与计算机工程系）
Chronoberg: A 250-Year Diachronic Corpus with Temporal Affective Lexicons for Language Model Analysis
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Department of Computer Science and Mathematics, University of Bremen（不莱梅大学计算机科学与数学系）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；Department of Computer Science, TU Darmstadt（图宾根大学计算机科学系）；Centre for Cognitive Science, TU Darmstadt（图宾根大学认知科学中心）；CERTAIN
Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons, Active Probing, and Large Language Models as Informants and Hypothesis Proposers
Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework
School of Information, Renmin University of China（中国人民大学信息学院）；Institute for Advanced Algorithms Research, Shanghai（上海高级算法研究院）；School of Information Technology and Management, University of International Business and Economics（对外经济贸易大学信息技术与管理学院）；Shanghai University of Finance and Economics（上海财经大学）
