@article{gao_preliminary_2017,
	title = {Preliminary {Exploration} of {Formula} {Embedding} for {Mathematical} {Information} {Retrieval}: can mathematical formulae be embedded like a natural language?},
	shorttitle = {Preliminary {Exploration} of {Formula} {Embedding} for {Mathematical} {Information} {Retrieval}},
	url = {http://arxiv.org/abs/1707.05154},
	abstract = {While neural network approaches are achieving breakthrough performance in the natural language related fields, there have been few similar attempts at mathematical language related tasks. In this study, we explore the potential of applying neural representation techniques to Mathematical Information Retrieval (MIR) tasks. In more detail, we first briefly analyze the characteristic differences between natural language and mathematical language. Then we design a "symbol2vec" method to learn the vector representations of formula symbols (numbers, variables, operators, functions, etc.) Finally, we propose a "formula2vec" based MIR approach and evaluate its performance. Preliminary experiment results show that there is a promising potential for applying formula embedding models to mathematical language representation and MIR tasks.},
	urldate = {2021-06-30},
	journal = {arXiv:1707.05154 [cs]},
	author = {Gao, Liangcai and Jiang, Zhuoren and Yin, Yue and Yuan, Ke and Yan, Zuoyu and Tang, Zhi},
	month = aug,
	year = {2017},
	note = {arXiv: 1707.05154},
	keywords = {embedding, ⛔ No DOI found},
}

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