Zipf's law in 50 languages: its structural pattern, linguistic interpretation, and cognitive motivation
Communication University of China（中国传媒大学）；Anhui Jianzhu University（安徽建筑大学）；Zhejiang University（浙江大学）；Ningbo Institute of Technology, Zhejiang University（浙江大学宁波理工学院）
Zipf's law has been found in many human-related fields, including language, where the frequency of a word is persistently found as a power law function of its frequency rank, known as Zipf's law. However, there is much dispute whether it is a universal law or a statistical artifact, and little is known about what mechanisms may have shaped it. To answer these questions, this study conducted a large scale cross language investigation into Zipf's law. The statistical results show that Zipf's laws in 50 languages all share a 3-segment structural pattern, with each segment demonstrating distinctive linguistic properties and the lower segment invariably bending downwards to deviate from theoretical expectation. This finding indicates that this deviation is a fundamental and universal feature of word frequency distributions in natural languages, not the statistical error of low frequency words. A computer simulation based on the dual-process theory yields Zipf's law with the same structural pattern, suggesting that Zipf's law of natural languages are motivated by common cognitive mechanisms. These results show that Zipf's law in languages is motivated by cognitive mechanisms like dual-processing that govern human verbal behaviors.
Phase transitions in a decentralized graph-based approach to human language
Pontificia Universidad Católica de Valparaíso（瓦尔帕莱索天主教大学）；Centro de Investigación DAiTA Lab（DAiTA实验室研究中心）；Facultad de Estudios Interdisciplinarios, Universidad Mayor（马约尔大学跨学科研究学院）；Escuela de Ingeniería Informática Pontificia Universidad Católica de Valparaíso（瓦尔帕莱索天主教大学信息工程学院）
Beyond Zipf's law: Modeling the structure of human language
IFISC (CSIC-UIB) Instituto de Física Interdisciplinar y Sistemas Complejos（IFISC（西班牙国家研究委员会-巴利阿里群岛大学）跨学科物理与复杂系统研究所）；Indiana University（印第安纳大学）；ISI Foundation（ISI基金会）
G. K.齐普夫对词频与其他词的特征之间关系的研究导致了各种语言学规律的制定。最流行的是Zipf的词频定律。在这里，我们集中在两个法律，已经研究得不太深入：意义频率的法律，即更频繁的词是多义的趋势，和缩写的法律，即更频繁的词是更短的趋势。在以前的工作中，我们测试了这些Zipfian定律对英语的鲁棒性，粗略地测量了字长的字符数，并区分了成人和儿童的语音。在本文中，我们将我们的研究扩展到其他语言（荷兰语和西班牙语），并介绍了两个额外的长度措施：音节长度和音素长度。相关分析表明，意义频率规律和缩略语规律在所有被分析的语言中总体上是一致的。
Departamento de Física, Universidade Federal de Pernambuco（物理系，佩纳伯库联邦大学）；Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco（巴雷里奥斯佩纳伯库教育科技研究所）
Word Sense Disambiguation for 158 Languages using Word Embeddings Only
Yandex（andex）
上下文中词义的消歧对人类来说很容易，但对自动方法来说是一个重大挑战。开发了复杂的监督和基于知识的模型来解决这一任务。然而，（i）给定单词的监督训练实例的固有Zipfian分布和/或（ii）语言知识表示的质量激发了完全无监督和无知识的词义消歧（WSD）方法的发展。它们对于资源不足的语言特别有用，这些语言没有任何资源来构建监督和/或基于知识的模型。在本文中，我们提出了一种方法，作为输入的标准预训练的词嵌入模型，并诱导一个完全成熟的词义库存，可用于在上下文消歧。我们使用这种方法在Grave et al.（2018）原始预训练的fastText词嵌入的基础上，为158种语言归纳了一系列意义清单，从而在这些语言中实现了WSD。模型和系统可在线获得。
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）
AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation
Social Data Science & AI Lab, LMU Munich（社会科学与人工智能实验室，慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Maryland, College Park（马里兰大学学院公园分校）；GESIS – Leibniz Institute for the Social Sciences（莱比锡社会科学研究机构）
Epistemological Fault Lines Between Human and Artificial Intelligence
Department of Computer Science, Sapienza University of Rome, Rome, Italy；Department of Psychology, University of Milan Bicocca, Milan, Italy；Faculty of Natural Sciences；Mathematics, University of Maribor, Maribor, Slovenia；Community Healthcare Center Dr. Adolf Drolc Maribor, Maribor, Slovenia；University College, Korea University, Seoul, Republic of Korea；Department of Physics, Kyung Hee University, Seoul, Republic of Korea
