poster

A Mathematical Information Retrieval System Based on RankBoost

Authors:

Ke Yuan,

Liangcai Gao,

Yuehan Wang,

Xiaohan Yi,

Zhi TangAuthors Info & Claims

JCDL '16: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries

Pages 259 - 260

https://doi.org/10.1145/2910896.2925460

Published: 19 June 2016 Publication History

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Abstract

Mathematical Information Retrieval (MIR) systems are designed to help users to find related formulae and further understand the formulae in scientific documents. However, in existing MIR systems, nearly all the ranker models of MIR systems are based on tf-idf model, and few efforts have been made to discover the features besides the relevance between the query formula and related formulae. In this paper, we investigate a supervised ranking approach (RankBoost) in an MIR system, and we consider not only the relevance between a query formula and related formulae, but also the features of the query formula itself and plentiful features about the documents where the related formulae appear. Experimental results show that our system achieves better performance by comparing with state-of-the-art MIR systems.

References

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Wang, Y., Gao, L., el at. WikiMirs 3.0: A Hybrid MIR System Based on the Context, Structure and Importance of Formulae in a Document. JCDL'15. 173--182. ACM. 2015.

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[2]

Lin, X., Gao, L., el at. A mathematics retrieval system for formulae in layout presentation. SIGIR. 697--706. ACM, 2014.

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Freund, Y., Iyer, R., et al. An efficient boosting algorithm for combining preferences. JMLR. 4: 933--969. 2003.

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Cao Z, Qin T, el at. Learning to rank: from pairwise approach to listwise approach. ICML'24. Pages:129--136. 2007.

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Cited By

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Dadure PPakray PBandyopadhyay S(2024)Mathematical Information Retrieval: A ReviewACM Computing Surveys10.1145/369995357:3(1-34)Online publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1145/3699953
Li JZeng HXiao COuyang CLiu H(2024)Listwise learning to rank method combining approximate NDCG ranking indicator with Conditional Generative Adversarial NetworksPattern Recognition Letters10.1016/j.patrec.2024.01.015179(31-37)Online publication date: Mar-2024
https://doi.org/10.1016/j.patrec.2024.01.015
Yuan KGao LJiang ZTang Z(2021)Formula Citation Graph Based Mathematical Information RetrievalDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86549-8_40(631-647)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86549-8_40
Show More Cited By

Index Terms

A Mathematical Information Retrieval System Based on RankBoost
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

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Published In

JCDL '16: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries

June 2016

316 pages

ISBN:9781450342292

DOI:10.1145/2910896

General Chairs:
Nabil R. Adam
Rutgers University
,
Boots Cassel
Villanova University
,
Yelena Yesha
University of Maryland, Baltimore County
,
Program Chairs:
Richard Furuta
Texas A&M University
,
Michele C. Weigle
Old Dominion University

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.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 June 2016

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Poster

Funding Sources

the National Natural Science Foun-dation of China
the Natural Science Foundation of Beijing
the Beijing Nova Program(2015)

Conference

JCDL '16

Sponsor:

JCDL '16: The 16th ACM/IEEE-CS Joint Conference on Digital Libraries

June 19 - 23, 2016

New Jersey, Newark, USA

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JCDL '16 Paper Acceptance Rate 15 of 52 submissions, 29%;

Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Citations

Cited By

View all

Dadure PPakray PBandyopadhyay S(2024)Mathematical Information Retrieval: A ReviewACM Computing Surveys10.1145/369995357:3(1-34)Online publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1145/3699953
Li JZeng HXiao COuyang CLiu H(2024)Listwise learning to rank method combining approximate NDCG ranking indicator with Conditional Generative Adversarial NetworksPattern Recognition Letters10.1016/j.patrec.2024.01.015179(31-37)Online publication date: Mar-2024
https://doi.org/10.1016/j.patrec.2024.01.015
Yuan KGao LJiang ZTang Z(2021)Formula Citation Graph Based Mathematical Information RetrievalDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86549-8_40(631-647)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86549-8_40
Peng SGao LYuan KTang Z(2021)Image to LaTeX with Graph Neural Network for Mathematical Formula RecognitionDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86331-9_42(648-663)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86331-9_42
Zhao WGao LYan ZPeng SDu LZhang Z(2021)Handwritten Mathematical Expression Recognition with Bidirectionally Trained TransformerDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86331-9_37(570-584)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86331-9_37
Yuan KGao LJiang ZTang ZChen JGonçalves MAllen JFox EKan MPetras V(2018)Formula Ranking within an ArticleProceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries10.1145/3197026.3197061(123-126)Online publication date: 23-May-2018
https://dl.acm.org/doi/10.1145/3197026.3197061
Davila KZanibbi RKando NSakai TJoho HLi Hde Vries AWhite R(2017)Layout and SemanticsProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080748(1165-1168)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080748

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A mathematics retrieval system for formulae in layout presentations

Recognition and retrieval of mathematical expressions

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