Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving

Doratossadat Dastgheib; Ehsaneddin Asgari

Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving

Doratossadat Dastgheib, Ehsaneddin Asgari

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Abstract

Extraction of supportive premises for a mathematical problem can contribute to profound success in improving automatic reasoning systems. One bottleneck in automated theorem proving is the lack of a proper semantic information retrieval system for mathematical texts. In this paper, we show the effect of keyword extraction in the natural language premise selection (NLPS) shared task proposed in TextGraph-16 that seeks to select the most relevant sentences supporting a given mathematical statement.

Anthology ID:: 2022.textgraphs-1.14
Volume:: Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
Month:: October
Year:: 2022
Address:: Gyeongju, Republic of Korea
Editors:: Dmitry Ustalov, Yanjun Gao, Alexander Panchenko, Marco Valentino, Mokanarangan Thayaparan, Thien Huu Nguyen, Gerald Penn, Arti Ramesh, Abhik Jana
Venue:: TextGraphs
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 124–126
Language:
URL:: https://aclanthology.org/2022.textgraphs-1.14/
DOI:
Bibkey:
Cite (ACL):: Doratossadat Dastgheib and Ehsaneddin Asgari. 2022. Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving. In Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing, pages 124–126, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):: Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving (Dastgheib & Asgari, TextGraphs 2022)
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PDF:: https://aclanthology.org/2022.textgraphs-1.14.pdf

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@inproceedings{dastgheib-asgari-2022-keyword,
    title = "Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving",
    author = "Dastgheib, Doratossadat  and
      Asgari, Ehsaneddin",
    editor = "Ustalov, Dmitry  and
      Gao, Yanjun  and
      Panchenko, Alexander  and
      Valentino, Marco  and
      Thayaparan, Mokanarangan  and
      Nguyen, Thien Huu  and
      Penn, Gerald  and
      Ramesh, Arti  and
      Jana, Abhik",
    booktitle = "Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.textgraphs-1.14/",
    pages = "124--126",
    abstract = "Extraction of supportive premises for a mathematical problem can contribute to profound success in improving automatic reasoning systems. One bottleneck in automated theorem proving is the lack of a proper semantic information retrieval system for mathematical texts. In this paper, we show the effect of keyword extraction in the natural language premise selection (NLPS) shared task proposed in TextGraph-16 that seeks to select the most relevant sentences supporting a given mathematical statement."
}

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%0 Conference Proceedings
%T Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving
%A Dastgheib, Doratossadat
%A Asgari, Ehsaneddin
%Y Ustalov, Dmitry
%Y Gao, Yanjun
%Y Panchenko, Alexander
%Y Valentino, Marco
%Y Thayaparan, Mokanarangan
%Y Nguyen, Thien Huu
%Y Penn, Gerald
%Y Ramesh, Arti
%Y Jana, Abhik
%S Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F dastgheib-asgari-2022-keyword
%X Extraction of supportive premises for a mathematical problem can contribute to profound success in improving automatic reasoning systems. One bottleneck in automated theorem proving is the lack of a proper semantic information retrieval system for mathematical texts. In this paper, we show the effect of keyword extraction in the natural language premise selection (NLPS) shared task proposed in TextGraph-16 that seeks to select the most relevant sentences supporting a given mathematical statement.
%U https://aclanthology.org/2022.textgraphs-1.14/
%P 124-126

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Markdown (Informal)

[Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving](https://aclanthology.org/2022.textgraphs-1.14/) (Dastgheib & Asgari, TextGraphs 2022)

Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving (Dastgheib & Asgari, TextGraphs 2022)

ACL

Doratossadat Dastgheib and Ehsaneddin Asgari. 2022. Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving. In Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing, pages 124–126, Gyeongju, Republic of Korea. Association for Computational Linguistics.