语言常常传达情感--情绪、感觉和态度。此外，不同的词可以传达不同程度的影响（强度）。然而，现有的手动创建的基本情绪（如愤怒和恐惧）的词典仅指示粗略类别的影响关联（例如，与愤怒相关联或不与愤怒相关联）。自动化的情感词典提供了很好的关联度，但它们往往不像人类创造的词典那样准确。在这里，我们第一次提出了一个手动创建的影响强度词汇与四种基本情绪的强度的实值分数：愤怒，恐惧，喜悦和悲伤。(We将随后添加更多情绪的条目，如厌恶，预期，信任和惊讶。我们把这个数据集称为NRC情感强度词典，简称AIL。AIL有近6,000个英语单词的条目。我们使用了一种称为最佳-最差缩放（BWS）的技术来创建词典。BWS提高了注释一致性，并获得了可靠的细粒度分数（分半可靠性> 0.91）。我们还将AIL中的条目与NRC VAD Lexicon中的条目进行了比较，NRC VAD Lexicon具有20 K英语单词的效价，唤醒和优势（VAD）分数。我们发现，愤怒、恐惧和悲伤的词平均来说具有非常相似的VAD分数。然而，悲伤的词往往比恐惧和愤怒的词有稍微低的优势分数。情感强度词典在诸如商业、教育、情报和公共卫生等许多领域的自动情感分析中有应用。AIL在构建自然语言生成系统中也是有用的。
英文摘要：
Words often convey affect -- emotions, feelings, and attitudes. Further, different words can convey affect to various degrees (intensities). However, existing manually created lexicons for basic emotions (such as anger and fear) indicate only coarse categories of affect association (for example, associated with anger or not associated with anger). Automatic lexicons of affect provide fine degrees of association, but they tend not to be accurate as human-created lexicons. Here, for the first time, we present a manually created affect intensity lexicon with real-valued scores of intensity for four basic emotions: anger, fear, joy, and sadness. (We will subsequently add entries for more emotions such as disgust, anticipation, trust, and surprise.) We refer to this dataset as the NRC Affect Intensity Lexicon, or AIL for short. AIL has entries for close to 6,000 English words. We used a technique called best-worst scaling (BWS) to create the lexicon. BWS improves annotation consistency and obtains reliable fine-grained scores (split-half reliability > 0.91). We also compare the entries in AIL with the entries in the NRC VAD Lexicon, which has valence, arousal, and dominance (VAD) scores for 20K English words. We find that anger, fear, and sadness words, on average, have very similar VAD scores. However, sadness words tend to have slightly lower dominance scores than fear and anger words. The Affect Intensity Lexicon has applications in automatic emotion analysis in a number of domains such as commerce, education, intelligence, and public health. AIL is also useful in the building of natural language generation systems.
Neighboring Words Affect Human Interpretation of Saliency Explanations
Bar Ilan University（巴伊兰大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）；University of Stuttgart（斯图加特大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly（南洋理工大学-不列颠哥伦比亚大学老年积极生活卓越联合研究中心）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-新加坡南洋理工大学联合研究院）；Nanyang Technological University（南洋理工大学）
A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories
Department of Electronics, Information, and Bioengineering, Polytechnic University of Milan（米兰理工学院电子、信息与生物工程系）；Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz（莱茵河畔林茨约翰·凯撒大学计算感知研究所多媒体挖掘与检索小组）；Department of Music Pedagogy, Nuremberg University of Music（纽伦堡音乐学院音乐教育系）；Human-centered AI Group, AI Lab, Linz Institute of Technology（林茨技术学院以人为本的人工智能小组）
Identifying Intensity of the Structure and Content in Tweets and the Discriminative Power of Attributes in Context with Referential Translation Machines
AI Enablement（AI赋能部门）；Huawei Türkiye R&D Center（华为土耳其研发中心）
Multiplex model of mental lexicon reveals explosive learning in humans
Institute for Complex Systems Simulation, University of Southampton（南安普顿大学复杂系统模拟研究所）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；Department of Electrical Engineering and Computer Science, University of Kansas（堪萨斯大学电气工程与计算机科学系）；School of Computer Science and Mathematics, Universitat Rovira i Virgili（罗维拉-威尔吉利大学计算机科学与数学学院）
