Cognitive models of the time-varying lexicon

IBM沃森或苹果Siri等现代人工智能系统已经显示出理解人类语言的前景。然而，人类语言的一个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汇，而人类词汇随时间变化。特别是，随着时间的推移，单词的含义表现出显著的流动性，例如，gay（“快乐”“同性恋”），grasp（“行动”“理解”）。是什么认知基础使这成为可能？解决这个问题对于理解语言的创造性本质至关重要，并为未来的人工智能研究设计类似人类的词典提供信息。与语言变化的其他方面相比，如语法或语音，其中有丰富的形式表征，历史意义的变化是最不了解的。一方面，意义变化的认知理论通常受到原则性形式化和尺度上的批判性评估的双重挑战。另一方面，虽然最近在计算语言学的工作已经显示出在检测新的单词意义的承诺，这样的工作通常不指定的认知机制，产生新兴的意义。** 本研究项目旨在通过开发预测词义演变的认知基础模型来弥合这些领域。它将追求的长期目标是发展词汇演变的正式帐户，短期重点是理解历史意义变化的原则机制。该方法将建立在我在机器学习和计算认知科学的背景，以及最近在这一领域的研究专长。具体而言，它将借鉴认知语言学理论和计算机科学的正式工具的组合，并从大型开放获取资源（包括历史语料库，词典和跨语言词典）到行为实验等丰富的经验数据。结果将有助于理论和建模的认知原则和机制，支持意义随着时间的推移而变化，探索在三套拟议的项目在个别单词，语义场和词汇的增量水平。* 该研究项目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，它将培养学术界基础科研的学者和将为工业和其他领域做出贡献的计算科学家。

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.