Evaluating and Improving the Extraction of Mathematical Identifier Definitions

Schubotz, Moritz; Krämer, Leonard; Meuschke, Norman; Hamborg, Felix; Gipp, Bela

Evaluating and Improving the Extraction of Mathematical Identifier Definitions

Dateien

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Datum

2017

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

JONES, Gareth J.F., ed. and others. Experimental IR Meets Multilinguality, Multimodality, and Interaction : 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11-14, 2017, Proceedings. Cham: Springer, 2017, pp. 82-94. Lecture notes in computer science. 10456. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-65813-1. Available under: doi: 10.1007/978-3-319-65813-1_7

Zusammenfassung

Mathematical formulae in academic texts significantly contribute to the overall semantic content of such texts, especially in the fields of Science, Technology, Engineering and Mathematics. Knowing the definitions of the identifiers in mathematical formulae is essential to understand the semantics of the formulae. Similar to the sense-making process of human readers, mathematical information retrieval systems can analyze the text that surrounds formulae to extract the definitions of identifiers occurring in the formulae. Several approaches for extracting the definitions of mathematical identifiers from documents have been proposed in recent years. So far, these approaches have been evaluated using different collections and gold standard datasets, which prevented comparative performance assessments. To facilitate future research on the task of identifier definition extraction, we make three contributions. First, we provide an automated evaluation framework, which uses the dataset and gold standard of the NTCIR-11 Math Retrieval Wikipedia task. Second, we compare existing identifier extraction approaches using the developed evaluation framework. Third, we present a new identifier extraction approach that uses machine learning to combine the wellperforming features of previous approaches. The new approach increases the precision of extracting identifier definitions from 17.85% to 48.60%, and increases the recall from 22.58% to 28.06%. The evaluation framework, the dataset and our source code are openly available at: https:// ident.formulasearchengine.com.

Fachgebiet (DDC)

004 Informatik

Konferenz

CLEF 2017, 11. Sept. 2017 - 14. Sept. 2017, Dublin, Ireland

Zitieren

ISO 690

SCHUBOTZ, Moritz, Leonard KRÄMER, Norman MEUSCHKE, Felix HAMBORG, Bela GIPP, 2017. Evaluating and Improving the Extraction of Mathematical Identifier Definitions. CLEF 2017. Dublin, Ireland, 11. Sept. 2017 - 14. Sept. 2017. In: JONES, Gareth J.F., ed. and others. Experimental IR Meets Multilinguality, Multimodality, and Interaction : 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11-14, 2017, Proceedings. Cham: Springer, 2017, pp. 82-94. Lecture notes in computer science. 10456. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-65813-1. Available under: doi: 10.1007/978-3-319-65813-1_7

BibTex

@inproceedings{Schubotz2017Evalu-41943,
  year={2017},
  doi={10.1007/978-3-319-65813-1_7},
  title={Evaluating and Improving the Extraction of Mathematical Identifier Definitions},
  number={10456},
  isbn={978-3-319-65813-1},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture notes in computer science},
  booktitle={Experimental IR Meets Multilinguality, Multimodality, and Interaction : 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11-14, 2017, Proceedings},
  pages={82--94},
  editor={Jones, Gareth J.F.},
  author={Schubotz, Moritz and Krämer, Leonard and Meuschke, Norman and Hamborg, Felix and Gipp, Bela}
}

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