Skip to main content

To Cite, or Not to Cite? Detecting Citation Contexts in Text

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10772)

Abstract

Recommending citations for scientific texts and other texts such as news articles has recently attracted considerable amount of attention. However, typically, the existing approaches for citation recommendation do not explicitly incorporate the question of whether a given context (e.g., a sentence), for which citations are to be recommended, actually “deserves” citations. Determining the “cite-worthiness” for each potential citation context as a step before the actual citation recommendation is beneficial, as (1) it can reduce the number of costly recommendation computations to a minimum, and (2) it can more closely approximate human-citing behavior, since neither too many nor too few recommendations are provided to the user. In this paper, we present a method based on a convolutional recurrent neural network for classifying potential citation contexts. Our experiments show that we can significantly outperform the baseline solution [1] and reduce the number of citation recommendations to about 1/10.

Keywords

  • Citation context
  • Citation recommendation
  • Recommender systems
  • Deep learning

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-76941-7_50
  • Chapter length: 6 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-76941-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.99
Price excludes VAT (USA)
Fig. 1.

Notes

  1. 1.

    The source code is available online at https://github.com/agrafix/grabcite-net.

  2. 2.

    All data sets are available online at http://citation-recommendation.org/publications.

  3. 3.

    http://www.comp.nus.edu.sg/~sugiyama/SchPaperRecData.html.

  4. 4.

    http://acl-arc.comp.nus.edu.sg/.

References

  1. Sugiyama, K., Kumar, T., Kan, M.Y., Tripathi, R.C.: Identifying citing sentences in research papers using supervised learning. In: CAMP 2010, pp. 67–72. IEEE (2010)

    Google Scholar 

  2. Teufel, S., Siddharthan, A., Tidhar, D.: An annotation scheme for citation function. In: SIGdial 2009, pp. 80–87 (2009)

    Google Scholar 

  3. Hu, Z., Chen, C., Liu, Z.: Where are citations located in the body of scientific articles? A study of the distributions of citation locations. J. Inf. 7(4), 887–896 (2013)

    CrossRef  Google Scholar 

  4. Angrosh, M.A., Cranefield, S., Stanger, N.: Context identification of sentences in related work sections using a conditional random field: towards intelligent digital libraries. In: JCDL 2010, pp. 293–302 (2010)

    Google Scholar 

  5. Teufel, S., Siddharthan, A., Tidhar, D.: Automatic classification of citation function. In: EMNLP 2007, pp. 103–110 (2006)

    Google Scholar 

  6. Valenzuela, M., Ha, V., Etzioni, O.: Identifying meaningful citations. In: SBD 2015 (2015)

    Google Scholar 

  7. Fisas, B., Saggion, H., Ronzano, F.: On the discoursive structure of computer graphics research papers. In: LAW@NAACL-HLT 2015, pp. 42–51 (2015)

    Google Scholar 

  8. Beel, J., Gipp, B., Langer, S., Breitinger, C.: Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2016)

    CrossRef  Google Scholar 

  9. Ebesu, T., Fang, Y.: Neural citation network for context-aware citation recommendation. In: SIGIR 2017, pp. 1093–1096 (2017)

    Google Scholar 

  10. Jiang, Z., Liu, X., Gao, L.: Chronological citation recommendation with information-need shifting. In: CIKM 2015, pp. 1291–1300 (2015)

    Google Scholar 

  11. Huang, W., Wu, Z., Chen, L., Mitra, P., Giles, C.L.: A neural probabilistic model for context based citation recommendation. In: AAAI 2015, pp. 2404–2410 (2015)

    Google Scholar 

  12. Alvarez, M.H., Gómez, J.M.: Survey about citation context analysis: tasks, techniques, and resources. Nat. Lang. Eng. 22(3), 327–349 (2016)

    CrossRef  Google Scholar 

  13. Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: AAAI 2015, pp. 2267–2273 (2015)

    Google Scholar 

  14. Zhou, G., Wu, J., Zhang, C., Zhou, Z.: Minimal gated unit for recurrent neural networks. CoRR abs/1603.09420 (2016)

    Google Scholar 

  15. Färber, M., Thiemann, A., Jatowt, A.: A high-quality gold standard for citation-based tasks. In: LREC 2018 (2018)

    Google Scholar 

  16. Bast, H., Korzen, C.: A benchmark and evaluation for text extraction from PDF. In: JCDL 2017, pp. 99–108 (2017)

    Google Scholar 

Download references

Acknowledgements

Michael Färber is an International Research Fellow of the Japan Society for the Promotion of Science (JSPS). The work was partially supported by MIC SCOPE (171507010). The Titan Xp used for this research was donated by the NVIDIA Corporation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Färber .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Färber, M., Thiemann, A., Jatowt, A. (2018). To Cite, or Not to Cite? Detecting Citation Contexts in Text. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-76941-7_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76940-0

  • Online ISBN: 978-3-319-76941-7

  • eBook Packages: Computer ScienceComputer Science (R0)