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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
