Universal knowledge engines for lexical semantic creativity in humans and machines

这项拟议的研究调查了世界各地语言词典中共享的知识结构，从而旨在开发通用的知识引擎，以增强对人类语言的机器解释。人类语言用户与人工自然语言系统的一个关键方面是我们解释和创造新词用法的能力，这一创造性特征反映在世界语言的词汇语义结构中。这种现象被称为词义扩展，对机器理解自然语言最基本的单位：词提出了巨大的挑战。这一合作研究计划将通过两个主要步骤，在弥合人类和机器在词汇语义创造力方面的差距方面展开新的努力。1)我们将大规模地表征词义扩展中的知识表征，这些知识表征在世界各地的语言中共享，并通过儿童语言习得，从各种公开可访问的场所提取数据和资源，包括跨语言数据库、心理学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式，以构建通用的知识引擎，以支持跨语言的灵活和适应性的新词含义解释和推理。

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.