We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the Scholastic Aptitude Test (SAT). A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly answers 47% of a collection of 374 college-level analogy questions (random guessing would yield 20% correct). We motivate this research by relating it to work in cognitive science and linguistics, and by applying it to a difficult problem in natural language processing, determining semantic relations in noun-modifier pairs. The problem is to classify a noun-modifier pair, such as "laser printer", according to the semantic relation between the noun (printer) and the modifier (laser). We use a supervised nearest-neighbour algorithm that assigns a class to a given noun-modifier pair by finding the most analogous noun-modifier pair in the training data. With 30 classes of semantic relations, on a collection of 600 labeled noun-modifier pairs, the learning algorithm attains an F value of 26.5% (random guessing: 3.3%). With 5 classes of semantic relations, the F value is 43.2% (random: 20%). The performance is state-of-the-art for these challenging problems.
Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships
University of Westminster（威斯敏斯特大学）；Oakland University（奥克兰大学）
随着自然语言处理（Natural Language Processing，NLP）技术的不断发展，语义关系的提取成为近年来的研究热点。这项工作的主要动机是这样一个事实，即建立知识图谱（KG）和基地（KB），作为智能应用程序的一个关键组成部分，是一个永无止境的挑战，因为新的知识需要收获，而旧的知识需要修改。目前，从文本中提取关系抽取的方法主要是神经模型，这些神经模型在大型语料库的机器学习中进行某种远程（弱）监督，无论是否咨询外部知识源。在本文中，我们实证研究和探索使用经典语义空间和模型的新想法的潜力，例如，词嵌入，用于提取词关联，结合关系抽取方法。我们的目标是使用这些词关联模型，以加强目前的关系抽取的方法。我们认为，这是第一次尝试这种研究的结果应该在一定程度上阐明这些词的关联模型可以使用，以及最有前途的类型的关系，以考虑提取。
Relation Extraction Model Based on Semantic Enhancement Mechanism
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications（计算机学院（国家试点软件工程学院）、北京邮电大学）；Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia（智能电信软件与多媒体北京重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）
R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；University of Science and Technology of China（中国科学技术大学）
