This paper introduces Latent Relational Analysis (LRA), a method for measuring semantic similarity. LRA measures similarity in the semantic relations between two pairs of words. When two pairs have a high degree of relational similarity, they are analogous. For example, the pair cat:meow is analogous to the pair dog:bark. There is evidence from cognitive science that relational similarity is fundamental to many cognitive and linguistic tasks (e.g., analogical reasoning). In the Vector Space Model (VSM) approach to measuring relational similarity, the similarity between two pairs is calculated by the cosine of the angle between the vectors that represent the two pairs. The elements in the vectors are based on the frequencies of manually constructed patterns in a large corpus. LRA extends the VSM approach in three ways: (1) patterns are derived automatically from the corpus, (2) Singular Value Decomposition is used to smooth the frequency data, and (3) synonyms are used to reformulate word pairs. This paper describes the LRA algorithm and experimentally compares LRA to VSM on two tasks, answering college-level multiple-choice word analogy questions and classifying semantic relations in noun-modifier expressions. LRA achieves state-of-the-art results, reaching human-level performance on the analogy questions and significantly exceeding VSM performance on both tasks.
Methods for Computing Legal Document Similarity: A Comparative Study
Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；Tata Research Development and Design Centre (TRDDC)（塔塔研究开发与设计中心（TRDDC））；Data61, CSIRO（澳大利亚联邦科学与工业研究组织 Data61）；Cyber Security CRC（网络安全合作研究中心）
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Fudan University（复旦大学）；Shenzhen Loop Area Institute（深圳河套学院）；Shanghai Jiao Tong University（上海交通大学）；Central South University（中南大学）；Huazhong Agricultural University（华中农业大学）
Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities
School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院）；Insight Centre for Data Analytics, University College Dublin（都柏林大学学院洞察数据分析中心）；School of Economics and Management, Beihang University（北京航空航天大学经济管理学院）
Similarity of Neural Network Models: A Survey of Functional and Representational Measures
University of Passau（帕绍大学）；University of Mannheim（曼海姆大学）；RWTH Aachen University（亚琛工业大学）；GESIS - Leibniz Institute for the Social Sciences（GESIS莱布尼茨社会科学研究所）；Complexity Science Hub Vienna（维也纳复杂性科学中心）
Measuring the Measures: Discriminative Capacity of Representational Similarity Metrics Across Model Families
Department of Computer Science and Engineering, UC San Diego（计算机科学与工程系，UC San Diego）；Department of Electrical and Computer Engineering, UC San Diego（电气与计算机工程系，UC San Diego）；Department of Cognitive Science, UC San Diego（认知科学系，UC San Diego）
Measuring IIA Violations in Similarity Choices with Bayesian Models
Department of Computer and Systems Engineering（计算机与系统工程系）；Federal University of Rio de Janeiro (UFRJ)（里约热内卢联邦大学）；School of Computer and Communication Sciences（计算机与通信科学学院）；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
A Methodological Framework for Measuring Spatial Labeling Similarity
School of Statistics and Mathematics, Zhongnan University of Economics and Law（中南财经政法大学统计与数学学院）；Department of Biomedical Engineering, Southern University of Science and Technology（南方科技大学生物医学工程系）；School of Information Engineering, Zhongnan University of Economics and Law（中南财经政法大学信息工程学院）；School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of science（中国科学院大学杭州高等研究院生命科学学院）
Measuring Similarity in Causal Graphs: A Framework for Semantic and Structural Analysis
MGH Institute for Technology Assessment（MGH技术评估研究所）；Harvard Medical School（哈佛医学院）；Sloan School of Management（斯隆管理学院）；Massachusetts Institute of Technology（麻省理工学院）
