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Interpretable Document Representations for Fast and Accurate Retrieval of Mathematical Information

Published: 11 July 2021 Publication History

Abstract

A study conducted by the International Data Corporation predicted that by the year 2021, the total amount of digital information resources would have reached the 40 zettabyte mark [2]. According to a rule formulated by Merrill Lynch, 80 to 90% of these resources are unstructured [7]. Despite this, users expect digital libraries to provide them with fast and interpretable access to digital information resources that will satisfy their information need. Math information retrieval emerged as a subfield of information retrieval in 2008 [8], when it became clear that standard information retrieval techniques used for text documents are inadequate to accurately retrieve documents in digital mathematical libraries.

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Gerard Salton and Chris Buckley. 1988. Term-weighting approaches in automatic text retrieval. ip&m, Vol. 24 (1988), 513--523. Issue 5.
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Christopher Shilakes and Julie Tylman. 1998. Enterprise information portals: industry overview. (16 November 1998), 354--362.
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Petr Sojka et al. 2018. mias: Math-aware retrieval in digital mathematical libraries. In Proceedings of cikm 2018 (Torino, Italy). acm, New York, usa, 1923--1926.
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Heinrich Stamerjohanns and Michael Kohlhase. 2008. Transforming the arkern-0.5ptχiv to xml. In Proceedings of cicm 2008. Springer, Birmingham, uk, 574--582.
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Ashish Vaswani et al. 2017. Attention is all you need. In Proceedings of nips 2017. Curran Associates Inc., Red Hook, ny, usa, 6000--6010.
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Richard Zanibbi et al. 2016. ntcir-12 mathir task overview. In Proceedings of ntcir-12, Vol. 12. nii, Tokyo, Japan, 299--308.

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cover image ACM Conferences
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2021
2998 pages
ISBN:9781450380379
DOI:10.1145/3404835
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 11 July 2021

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  1. digital mathematical libraries
  2. formula unification
  3. math information retrieval
  4. query expansion
  5. representation learning

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