How people think, feel, and behave, primarily is a representation of their personality characteristics. By being conscious of personality characteristics of individuals whom we are dealing with or decided to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications take place there. The most prominent tool in such communications, is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text a knowledge graph which is a set of interlinked descriptions of concepts, was built through matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving more powerful representation the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph which is now a knowledgeable alternative for the input text was embedded to yield an embedding matrix. Finally, to perform personality predictions the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short term memory (LSTM) and bidirectional long short term memory (BiLSTM). The results indicated a considerable improvements in prediction accuracies in all of the suggested classifiers.
以往的自动人格识别工作主要集中在使用传统的语言特征分类模型。然而，具有上下文嵌入的注意神经网络在文本分类中取得了巨大的成功，很少被探索用于这项任务。在这个项目中，我们有两个主要贡献。首先，我们创建了第一个基于对话的人格数据集，MysPersona，通过众包注释来自Friends TV Show的演讲者的5个人格特征。其次，我们提出了一种新的方法来自动人格识别使用预先训练的上下文嵌入（BERT和RoBERTa）和专注的神经网络。我们的模型在很大程度上将独白Essays数据集的最新结果提高了2.49%，并在我们的MonkeysPersona上建立了坚实的基准。通过比较两个数据集的结果，我们展示了在多方对话中建模个性的挑战。
HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction
Department of Computer Science and Information Technology（计算机科学与信息技术系）；Institude for Advanced Studies in Basic Sciences (IASBS)（基础科学高级研究 institute (IASBS)）
Automatic Item Generation for Personality Situational Judgment Tests with Large Language Models
Beijing Normal University（北京师范大学）；Central China Normal University（华中师范大学）；Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education（教育部青少年网络心理与行为重点实验室）；Beijing Key Laboratory of Applied Experimental Psychology（北京市应用实验心理学重点实验室）；Research Center for Capacity Building in Educational Assessment and Evaluation (Beijing Higher Education Innovation Center for Philosophy and Social Sciences)（教育评价与评估能力建设研究中心（北京高等教育哲学社会科学创新中心））
Automatic Conditional Generation of Personalized Social Media Short Texts
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Key Laboratory of Network System and Network Culture（网络系统与网络文化北京市重点实验室）；Ping An Technology (Shenzhen) Co., Ltd.（平安科技（深圳）有限公司）
Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach
Department of Computer Science, Stony Brook University（石溪大学计算机科学系）；Melbourne Graduate School of Education, The University of Melbourne（墨尔本大学墨尔本教育研究生院）；Judge Business School, University of Cambridge（剑桥大学贾吉商学院）；Graduate School of Business, Stanford University（斯坦福大学商学院）；Columbia Business School, Columbia University（哥伦比亚大学哥伦比亚商学院）；Computer and Information Science, University of Pennsylvania（宾夕法尼亚大学计算机与信息科学系）
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
Tsinghua University（清华大学）；The University of Melbourne（墨尔本大学）；Universitas Indonesia（印度尼西亚大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
