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Representing domain knowledge is crucial for any task. There has been a wide range of techniques developed to represent this knowledge, from older logic based approaches to the more recent deep learning based techniques (i.e. embeddings). In this paper, we discuss some of these methods, focusing on the representational expressiveness tradeoffs that are often made. In particular, we focus on the the ability of various techniques to encode `partial knowledge’
ensembles of embeddings' and aggregate embeddings’ and
demonstrate how they allow for partial knowledge.
生成讲解博客的中文版本

生成AI智能校验的中文版本