Universal knowledge engines for lexical semantic creativity in humans and machines

用于人类和机器词汇语义创造力的通用知识引擎

• 批准号: 576149-2022
• 负责人: Xu, YangYX
• 金额: $ 1.82万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750011
• 关键词: Universal; knowledge; engines; lexical; semantic

This proposed research investigates knowledge structures that are shared in the lexicons across the world's languages and thereby aims to develop universal knowledge engines that will enhance machine interpretation of human languages.A key aspect that distinguishes human language users from artificial natural language systems is our ability to interpret and create novel word usages, and this creative character is reflected in the lexical semantic structures of the world's languages. This phenomenon, known as word meaning extension, poses significant challenges for machine understanding of the most fundamental units of natural languages: words. This collaborative research program will launch a new effort in bridging the gap between humans and machines in lexical semantic creativity, by taking two main steps. 1) We will characterize at a large scale knowledge representations in word meaning extension that are shared across the world's languages and through child language acquisition, drawing data and resources from a variety of publicly accessible venues including cross-linguistic databases, psychological experiments, and deep learning models. 2) We will develop new methodologies that harness and integrate different knowledge modalities toward constructing universal knowledge engines to support the flexible and adaptive interpretation and inference of novel word meanings across languages.

这项拟议中的研究调查了世界上各种语言的词典中共享的知识结构，从而旨在开发通用的知识引擎，以增强机器对人类语言的解释能力。人类语言用户与人工自然语言系统的一个关键区别在于我们解释和创造新颖词语用法的能力，这种创造性特征反映在世界语言的词汇语义结构中。这种被称为词义扩展的现象，对机器理解自然语言最基本的单位--词--提出了重大挑战。这项合作研究计划将采取两个主要步骤，在弥合人类和机器在词汇语义创造力方面的差距方面做出新的努力。1)我们将在世界各地的语言和通过儿童语言习得共享的词义扩展中大规模表征知识表示，从各种公共访问场所绘制数据和资源，包括跨语言数据库，心理实验和深度学习模型。2)我们将开发新的方法，利用和整合不同的知识形态，构建通用的知识引擎，以支持跨语言的新词义的灵活和自适应的解释和推理。