Semantic Preserving Bijective Mappings of Mathematical Formulae Between Document Preparation Systems and Computer Algebra Systems. Cohl, H. S., Schubotz, M., Youssef, A., Greiner-Petter, A., Gerhard, J., Saunders, B. V., McClain, M. A., Bang, J., & Chen, K. In Geuvers, H., England, M., Hasan, O., Rabe, F., & Teschke, O., editors, Proceedings International Conference on Intelligent Computer Mathematics (CICM), volume 10383, of Lecture Notes in Computer Science, pages 115–131, Edinburgh, UK, 2017. Springer.
Paper doi abstract bibtex There are many different approaches to represent mathematical expressions on computers. Word processors like LaTeX offer the ability to render mathematical expressions as one would write these on paper. Using LaTeX, LaTeXML, and tools generated for use in the NIST Digital Library of Mathematical Functions, semantically enhanced mathematical LaTeX markup (semantic LaTeX) is achieved by using a semantic macro set. Computer algebra systems (CAS) such as Maple and Mathematica use alternative markup to represent mathematical expressions. For the conversion from semantic LaTeX to the CAS representations, we have adapted the approach of Part of Speech Tagging from Natural Language Processing. By taking advantage of Youssef's Part-of-Math Tagger, CAS internal representations, and locally developed software, we develop algorithms to convert between semantic LaTeX and representations from CAS. The goal of these efforts is to provide CAS formulae representations to the public for digital mathematics libraries. In connection with these efforts, we have developed software which has converted between CAS representations through semantic LaTeX to generate MediaWiki wikitext for the NIST Digital Repository of Mathematical Formulae for the Wolfram Encoding Continued Fraction dataset and the University of Antwerp Continued Fractions for Special Functions dataset.
@inproceedings{BibbaseCohlSYG17,
address = {Edinburgh, UK},
series = {Lecture {Notes} in {Computer} {Science}},
title = {Semantic {Preserving} {Bijective} {Mappings} of {Mathematical} {Formulae} {Between} {Document} {Preparation} {Systems} and {Computer} {Algebra} {Systems}},
volume = {10383},
isbn = {978-3-319-62074-9},
url = {https://arxiv.org/abs/2109.08655},
doi = {10/ggv8dk},
abstract = {There are many different approaches to represent mathematical expressions on
computers. Word processors like LaTeX offer the ability to render mathematical expressions as one
would write these on paper. Using LaTeX, LaTeXML, and tools generated for use in the NIST
Digital Library of Mathematical Functions, semantically enhanced mathematical
LaTeX markup (semantic LaTeX) is achieved by using a semantic macro set. Computer algebra
systems (CAS) such as Maple and Mathematica use alternative markup to represent
mathematical expressions. For the conversion from semantic LaTeX to the CAS representations, we
have adapted the approach of Part of Speech Tagging from Natural Language Processing. By taking
advantage of Youssef's Part-of-Math Tagger, CAS internal representations, and locally developed
software, we develop algorithms to convert between semantic LaTeX and representations from CAS.
The goal of these efforts is to provide CAS formulae representations to the public for digital
mathematics libraries. In connection with these efforts, we have developed software which has
converted between CAS representations through semantic LaTeX to generate MediaWiki wikitext for
the NIST Digital Repository of Mathematical Formulae for the Wolfram Encoding Continued
Fraction dataset and the University of Antwerp Continued Fractions for Special Functions dataset.},
urldate = {2021-09-08},
booktitle = {Proceedings {International} {Conference} on {Intelligent} {Computer} {Mathematics} ({CICM})},
publisher = {Springer},
author = {Cohl, Howard S. and Schubotz, Moritz and Youssef, Abdou and Greiner-Petter, Andre and Gerhard, Jürgen and Saunders, Bonita V. and McClain, Marjorie A. and Bang, Joon and Chen, Kevin},
editor = {Geuvers, Herman and England, Matthew and Hasan, Osman and Rabe, Florian and Teschke, Olaf},
year = {2017},
pages = {115--131},
}
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