A Computational Theory for Life-Long Learning of Semantics

2018/06/28 by Peter Sutor, Sutor, Peter, Douglas Summers-Stay +3 · 1 citation
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1806.10755

openalex publication_date 2018/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Semantic vectors are learned from data to express semantic relationships between elements of information, for the purpose of solving and informing downstream tasks. Other models exist that learn to map and classify supervised data. However, the two worlds of learning rarely interact to inform one another dynamically, whether across types of data or levels of semantics, in order to form a unified model. We explore the research problem of learning these vectors and propose a framework for learning the semantics of knowledge incrementally and online, across multiple mediums of data, via binary vectors. We discuss the aspects of this framework to spur future research on this approach and problem.

Citations

• Character-Aware Neural Language Models

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