Cognitive models of the time-varying lexicon

时变词典的认知模型

• 批准号: RGPIN-2018-05872
• 负责人: Xu, Yang
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=750067
• 关键词: Cognitive; models; time; varying; lexicon

Modern artificial intelligent systems such as IBM Watson or Apple Siri have shown promise of understanding human language. However, a fundamental property of human language challenges the key assumptions in these technologies: A natural language system typically relies on a fixed lexicon, in contrast with the human lexicon that varies over time. In particular, words have shown remarkable fluidity in meaning over time, e.g., gay ('happy''homosexual'), grasp ('action''understanding'). What are the cognitive foundations that make this possible? Addressing this question will be centrally important to the understanding of the creative nature of language and inform future AI research in the design of a human-like lexicon.Compared to other aspects of language change such as in grammar or sound where there are rich formal characterizations, historical meaning change is least understood. On one hand, cognitive theories of meaning change typically suffer from the dual challenge of principled formalization and critical assessment at scale. On the other hand, although recent work in computational linguistics has shown promise in the detection of novel word meanings, such work does not typically specify the cognitive mechanisms that give rise to emerging meanings. This research program is situated at bridging these fields by developing cognitively grounded models that predict the evolution of word meanings. It will pursue the long term objective of developing a formal account of the evolution of the lexicon, with the short-term emphasis on understanding the principled mechanisms of historical meaning change.The approach will build upon my background in machine learning and computational cognitive science, and recent research expertise in this area. Specifically, it will draw on a combination of cognitive linguistic theories and formal tools from computer science, and engage rich empirical data ranging from large open-access resources including historical corpora, dictionaries and cross-linguistic lexicons, to behavioral experiments. The outcomes will contribute to theories and modeling of the cognitive principles and mechanisms that support meaning change over time, explored in three sets of proposed projects at the incremental levels of individual words, semantic fields, and the lexicon. The interdisciplinary nature of this research program will have a significant impact on the fields of cognitive science, linguistics, and artificial intelligence. As such, it will train both scholars for basic scientific research in academia and computational scientists who will contribute to industry and beyond.

IBM Watson或Apple Siri等现代人工智能系统已经显示出理解人类语言的希望。但是，人类语言的基本属性挑战了这些技术中的关键假设：自然语言系统通常依赖于固定的词典，而随着时间的流逝，人类词典也会变化。特别是，随着时间的流逝，言语在含义上表现出显着的流动性，例如，盖伊（'Happy'''Homosexual'），掌握（'Action'nodervanding'）。什么是认知基础？解决这个问题将对对语言的创造性的理解并为未来的AI研究提供类似人类的词典的设计对语言变化的其他方面（例如在语法或声音中）等方面的研究，这将非常重要。 ，历史含义变化最少理解。一方面，意义变化的认知理论通常遭受原则形式化和大规模评估的双重挑战。另一方面，尽管计算语言学的最新工作在发现新单词含义方面已经有望，但这种工作通常并未指定引起新兴含义的认知机制。该研究计划位于通过开发预测单词含义演变的认知基础模型来桥接这些领域。它将追求长期的目标，以形成对词典演变的正式描述，短期强调理解历史意义的原则机制变化的原则机制。这种方法将基于我的机器学习和计算认知科学的背景，以及该领域的最新研究专业知识。具体而言，它将利用计算机科学的认知语言理论和正式工具的结合，并参与丰富的经验数据，包括大型开放式资源，包括历史语料库，词典和跨语言词典，到行为实验。结果将有助于含义随时间变化的认知原理和机制的理论和建模，并以单个单词，语义领域和词典的三组项目探索。该研究计划的跨学科性质将对认知科学，语言学和人工智能领域产生重大影响。因此，它将培训这两个学者，以供学术界和计算科学家的基础科学研究，他们将为工业及以后做出贡献。