@inproceedings{alexeeva-etal-2020-mathalign,
title = "{M}ath{A}lign: Linking Formula Identifiers to their Contextual Natural Language Descriptions",
author = "Alexeeva, Maria and
Sharp, Rebecca and
Valenzuela-Esc{\'a}rcega, Marco A. and
Kadowaki, Jennifer and
Pyarelal, Adarsh and
Morrison, Clayton",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.269",
pages = "2204--2212",
abstract = "Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired. This entails segmenting mathematical formulae into identifiers and linking them to their natural language descriptions. We propose a rule-based approach for this task, which extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task, as well as the tool used to create it.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired. This entails segmenting mathematical formulae into identifiers and linking them to their natural language descriptions. We propose a rule-based approach for this task, which extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task, as well as the tool used to create it.</abstract>
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%0 Conference Proceedings
%T MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions
%A Alexeeva, Maria
%A Sharp, Rebecca
%A Valenzuela-Escárcega, Marco A.
%A Kadowaki, Jennifer
%A Pyarelal, Adarsh
%A Morrison, Clayton
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F alexeeva-etal-2020-mathalign
%X Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired. This entails segmenting mathematical formulae into identifiers and linking them to their natural language descriptions. We propose a rule-based approach for this task, which extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task, as well as the tool used to create it.
%U https://aclanthology.org/2020.lrec-1.269
%P 2204-2212
Markdown (Informal)
[MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions](https://aclanthology.org/2020.lrec-1.269) (Alexeeva et al., LREC 2020)
ACL