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        <title>Premise Selection in Natural Language Mathematical Texts</title>
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        <namePart type="given">Deborah</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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            <namePart type="given">Joyce</namePart>
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    <abstract>The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. The natural language premise selection task consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be useful to prove a particular statement. We propose an approach to solve this task as a link prediction problem, using Deep Convolutional Graph Neural Networks. This paper also analyses how different baselines perform in this task and shows that a graph structure can provide higher F1-score, especially when considering multi-hop premise selection.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.657</identifier>
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        <date>2020-07</date>
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            <start>7365</start>
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%0 Conference Proceedings
%T Premise Selection in Natural Language Mathematical Texts
%A Ferreira, Deborah
%A Freitas, André
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ferreira-freitas-2020-premise
%X The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. The natural language premise selection task consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be useful to prove a particular statement. We propose an approach to solve this task as a link prediction problem, using Deep Convolutional Graph Neural Networks. This paper also analyses how different baselines perform in this task and shows that a graph structure can provide higher F1-score, especially when considering multi-hop premise selection.
%R 10.18653/v1/2020.acl-main.657
%U https://aclanthology.org/2020.acl-main.657
%U https://doi.org/10.18653/v1/2020.acl-main.657
%P 7365-7374

Markdown (Informal)

[Premise Selection in Natural Language Mathematical Texts](https://aclanthology.org/2020.acl-main.657) (Ferreira & Freitas, ACL 2020)

• Premise Selection in Natural Language Mathematical Texts (Ferreira & Freitas, ACL 2020)

ACL

• Deborah Ferreira and André Freitas. 2020. Premise Selection in Natural Language Mathematical Texts. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7365–7374, Online. Association for Computational Linguistics.