A Computational Theory for Life-Long Learning of Semantics
This work addresses the challenge of unifying semantic learning across diverse data mediums, but it appears incremental as it builds on existing models without presenting new empirical results.
The paper tackles the problem of learning semantic vectors dynamically across different data types and semantic levels, proposing a framework for incremental and online learning using binary vectors.
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.