Abstract
We present a classifier for the Mathematics Subject Classification (MSC) system, combining techniques in unsupervised learning such as nearest neighbors, and supervised learning such as neural networks. We will discuss the challenges presented in the classification task, such as the large number of possible classes, many with overlapping scope; and describe the data processing and experimental methodologies employed.
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Acknowledgments
We would like to thank Jeremy Michelson and Michael Trott for continuously lending their ears and ideas throughout this project, and the ICMS reviewer for constructive comments on an earlier draft of this paper.
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Dong, Y. (2018). NLP-Based Detection of Mathematics Subject Classification. In: Davenport, J., Kauers, M., Labahn, G., Urban, J. (eds) Mathematical Software – ICMS 2018. ICMS 2018. Lecture Notes in Computer Science(), vol 10931. Springer, Cham. https://doi.org/10.1007/978-3-319-96418-8_18
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DOI: https://doi.org/10.1007/978-3-319-96418-8_18
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