Abstract
We present a recommender system covering math and math physics papers from the arXiv, to assist researchers to quickly retrieve theorems and discover similar results from this vast corpus. The retrieval aims to discover not just syntactic, but also semantic similarity. We will discuss the challenges encountered and the experimental methodologies used.
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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, as well as Rob Y. Lewis and the ICMS reviewer for helpful comments on an earlier draft of this paper.
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Dong, Y. (2018). NLP and Large-Scale Information Retrieval on Mathematical Texts. 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_19
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DOI: https://doi.org/10.1007/978-3-319-96418-8_19
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