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Entity Linking for Mathematical Expressions in Scientific Documents

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10075)

Abstract

This paper addresses the challenge of determining the identity of math expressions in scientific documents by linking these expressions to their corresponding Wikipedia articles. Math expressions are frequently used to denote important concepts in scientific documents, yet several of them, for example, famous equations, often have minimal explanation in the documents. This task will allow us to obtain an additional explanation from Wikipedia regarding these math expressions. This paper proposes an approach to this challenge, where the structures and surrounding text of math expressions are used for math entity linking. Our initial evaluation shows that a balanced combination of math structures and textual descriptions is required to obtain reliable linking performance.

Keywords

  • Knowledge acquisition
  • Math entity linking
  • Math expression similarity
  • Wikification

This work was supported by JSPS Kakenhi Grant Number 14J09896, and CREST, JST.

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Notes

  1. 1.

    http://www.swi-prolog.org/.

  2. 2.

    https://aclweb.org/anthology.

  3. 3.

    http://www.inftyreader.org.

  4. 4.

    http://dlmf.nist.gov/LaTeXML/.

References

  1. Agrawal, R., Gollapudi, S., Kannan, A., Kenthapadi, K.: Data mining for improving textbooks. SIGKDD Explor. Newsl. 13(2), 7–19 (2012)

    CrossRef  Google Scholar 

  2. Cheng, X., Roth, D.: Relational Inference for Wikification. In: Proceedings of EMNLP (2013)

    Google Scholar 

  3. Cucerzan, S.: Large-scale named entity disambiguation based on wikipedia data. In: Proceedings of the Joint Conference of EMNLP-CoNLL (2007)

    Google Scholar 

  4. Kristianto, G.Y., Topić, G., Aizawa, A.: The MCAT math retrieval system for NTCIR-12 MathIR task. In: Proceedings of the 12th NTCIR Conference (2016)

    Google Scholar 

  5. Mihalcea, R., Csomai, A., Wikify!: Linking documents to encyclopedic knowledge. In: Proceedings of the 16th ACM CIKM (2007)

    Google Scholar 

  6. Milne, D., Witten, I.H.: Learning to link with wikipedia. In: Proceedings of the 17th ACM CIKM (2008)

    Google Scholar 

  7. Ni, Y., Xu, Q.K., Cao, F., Mass, Y., Sheinwald, D., Zhu, H.J., Cao, S.S.: Semantic documents relatedness using concept graph representation. In: Proceedings of the 9th ACM WSDM (2016)

    Google Scholar 

  8. Pagel, R., Schubotz, M.: Mathematical language processing project. In: Work in Progress Track at CICM (2014)

    Google Scholar 

  9. Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic Keyword Extraction from Individual Documents. Applications and Theory, In Text Mining (2010)

    Google Scholar 

  10. Zanibbi, R., Aizawa, A., Kohlhase, M., Ounis, I., Topić, G., Davila, K.: NTCIR-12 MathIR task overview. In: NTCIR, National Institute of Informatics (NII) (2016)

    Google Scholar 

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Correspondence to Giovanni Yoko Kristianto .

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Kristianto, G.Y., Topić, G., Aizawa, A. (2016). Entity Linking for Mathematical Expressions in Scientific Documents. In: Morishima, A., Rauber, A., Liew, C. (eds) Digital Libraries: Knowledge, Information, and Data in an Open Access Society. ICADL 2016. Lecture Notes in Computer Science(), vol 10075. Springer, Cham. https://doi.org/10.1007/978-3-319-49304-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-49304-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49303-9

  • Online ISBN: 978-3-319-49304-6

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