Variable Typing: Assigning Meaning to Variables in Mathematical Text. Stathopoulos, Y., Baker, S., Rei, M., & Teufel, S. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pages 303–312, New Orleans, Louisiana, USA, 2018. ACM. ![Variable Typing: Assigning Meaning to Variables in Mathematical Text [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Information about the meaning of mathematical variables in text is useful in NLP/IR tasks such as symbol disambiguation, topic modeling and mathematical information retrieval (MIR). We introduce variable typing, the task of assigning one mathematical type (multi-word technical terms referring to mathematical concepts) to each variable in a sentence of mathematical text. As part of this work, we also introduce a new annotated data set composed of 33,524 data points extracted from scientific documents published on arXiv. Our intrinsic evaluation demonstrates that our data set is sufficient to successfully train and evaluate current classifiers from three different model architectures. The best performing model is evaluated on an extrinsic task: MIR, by producing a typed formula index. Our results show that the best performing MIR models make use of our typed index, compared to a formula index only containing raw symbols, thereby demonstrating the usefulness of variable typing.
@inproceedings{stathopoulos_variable_2018,
address = {New Orleans, Louisiana, USA},
title = {Variable {Typing}: {Assigning} {Meaning} to {Variables} in {Mathematical} {Text}},
shorttitle = {Variable {Typing}},
url = {https://doi.org/10.18653/v1/n18-1028},
doi = {10/gn3sxt},
abstract = {Information about the meaning of mathematical variables in text is useful in NLP/IR tasks such as symbol disambiguation, topic modeling and mathematical information retrieval (MIR). We introduce variable typing, the task of assigning one mathematical type (multi-word technical terms referring to mathematical concepts) to each variable in a sentence of mathematical text. As part of this work, we also introduce a new annotated data set composed of 33,524 data points extracted from scientific documents published on arXiv. Our intrinsic evaluation demonstrates that our data set is sufficient to successfully train and evaluate current classifiers from three different model architectures. The best performing model is evaluated on an extrinsic task: MIR, by producing a typed formula index. Our results show that the best performing MIR models make use of our typed index, compared to a formula index only containing raw symbols, thereby demonstrating the usefulness of variable typing.},
booktitle = {Proceedings of the 2018 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies} ({NAACL}-{HLT})},
publisher = {ACM},
author = {Stathopoulos, Y. and Baker, S. and Rei, Marek and Teufel, S.},
year = {2018},
keywords = {identifier, mathir},
pages = {303--312},
}
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