Abstract
Word embedding, which represents individual words with semantically rich numerical vectors, has made it possible to successfully apply deep learning to NLP tasks such as semantic role modeling, question answering, and machine translation. As math text consists of natural text as well as math expressions that similarly exhibit linear correlation and contextual characteristics, word embedding can be applied to math documents as well. On the other hand, math terms also show characteristics (e.g., abstractions) that are different from textual words. Accordingly, it is worthwhile to explore the use and effectiveness of word embedding in math language processing and MKM.
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