@inproceedings{BibbaseGreinerPetterRSA19,
	address = {Paris, France},
	title = {Why {Machines} {Cannot} {Learn} {Mathematics}, {Yet}},
	volume = {2414},
	url = {http://ceur-ws.org/Vol-2414/paper14.pdf},
	abstract = {Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods. However, while mathematics is a precise and accurate science, it is usually expressed by less accurate and imprecise descriptions. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. In this work, we apply text embedding techniques to the arXiv collection of STEM documents and explore how these are unable to properly understand mathematics from that corpus, while proposing alternative to mitigate such situation.},
	booktitle = {Proceedings of the 4th {Joint} {Workshop} on {Bibliometric}-{Enhanced} {Information} {Retrieval} and {Natural} {Language} {Processing} for {Digital} {Libraries} ({BIRNDL}@{SIGIR})},
	publisher = {CEUR-WS.org},
	author = {Greiner-Petter, Andre and Ruas, Terry and Schubotz, Moritz and Aizawa, Akiko and Grosky, William I. and Gipp, Bela},
	year = {2019},
	note = {Core Rank A*},
}

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