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 SAT college entrance exam. 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; the average college-bound senior high school student answers about 57% correctly). We motivate this research 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 both verbal analogies and noun-modifier relations.
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Ricoh Software Research Center (Beijing)（理光软件研究所（北京））；School of Software, Harbin University of Science and Technology（哈尔滨理工大学软件学院）；Medical Records Room, the Second Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第二医院病案室）
Semantic Norm Recognition and its application to Portuguese Law
University of Lisbon（里斯本大学）；Instituto Superior Técnico（高等技术学院）；University of Algarve（阿尔加维大学）；Faculty of Science and Technology, University of Algarve（阿尔加维大学科学与技术学院）；CCMAR（海洋科学研究中心）；INESC-ID（里斯本系统与计算机工程、技术与科学研究所）
En-ViMedNER: An English-Vietnamese Parallel Biomedical Corpus with UMLS Semantic Type Annotations
College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；Faculty of Engineering and IT, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；Center for AI Research, VinUniversity（VinUniversity人工智能研究中心）；Monash University, Australia（莫纳什大学）
1SimpleWay.AI 2McGill University 3University of Toronto 4University of California, Los Angeles 5The Chinese University of Hong Kong 6University of Manitoba 7Universit\'e de Montr\'eal 8Boston University 9Mila - Quebec AI Institute 10CG Matrix Technology Limited 11Lakehead University 12McMaster University
Semantic Relatedness and Taxonomic Word Embeddings
Innovation Exchange（创新交流中心）；IBM Ireland（爱尔兰国际商业机器公司）；ADAPT Research Centre（ADAPT研究中心）；Technological University Dublin（都柏林理工大学）；Underwriters Laboratories（美国保险商实验室）
Semantic Segmentation of Legal Documents via Rhetorical Roles
Indian Institute of Technology Kanpur (IIT-K)（印度理工学院坎普尔分校）；West Bengal National University of Juridical Sciences (WBNUJS)（西孟加拉国立法律科学大学）；Indian Institute of Management Ranchi (IIM-R)（印度管理学院兰契分校）
