Skip to main content
SpringerLink
Log in

Investigating Citation Linkage as a Sentence Similarity Measurement Task Using Deep Learning

  • Conference paper
  • First Online:
Advances in Artificial Intelligence (Canadian AI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12109))

Included in the following conference series:

Abstract

Research publications reflect advancements in the corresponding research domain. In these research publications, scientists often use citations to bolster the presented research findings and portray the improvements that come with these findings, at the same time, to make the contents more understandable to the audience by navigating the flow of information. In the science domain, a citation refers to the document from where this information originates, but doesn’t specify the text span that is actually being cited. This paper develops a framework which can create a linkage between the citing sentences from the ongoing research article and the related cited sentences from the corresponding referenced documents. Eventually, it will reduce the burden of the readers to go through all the sentences in the documents while acquiring the required background information. This citation linkage problem has been modelled as a semantic relatedness task where given a citing sentence the framework generates the sentence pairs with the citing sentence and each of the sentences from the reference document and then tries to determine which sentence pair is semantically similar and which pair is not. Construction of the citation linkage framework involves corpus creation and utilizing deep-learning models for semantic similarity measurement.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. AbuRa’ed, A., Chiruzzo, L., Saggion, H.: What sentence are you referring to and why? Identifying cited sentences in scientific literature. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pp. 9–17 (2017)

    Google Scholar 

  2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

  3. Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)

    MATH  Google Scholar 

  4. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)

    Article  Google Scholar 

  5. Bonin, S., Petrera, F., Niccolini, B., Stanta, G.: PCR analysis in archival postmortem tissues. Mol. Pathol. 56(3), 184–186 (2003)

    Article  Google Scholar 

  6. Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364 (2017)

  7. Garfield, E.: Citation analysis as a tool in journal evaluation. Science 178(4060), 471–479 (1972)

    Article  Google Scholar 

  8. Garzone, M., Mercer, R.E.: Towards an automated citation classifier. In: Hamilton, H.J. (ed.) AI 2000. LNCS (LNAI), vol. 1822, pp. 337–346. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45486-1_28

    Chapter  Google Scholar 

  9. Houngbo, H., Mercer, R.E.: Investigating citation linkage with machine learning. In: Mouhoub, M., Langlais, P. (eds.) AI 2017. LNCS (LNAI), vol. 10233, pp. 78–83. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57351-9_10

    Chapter  Google Scholar 

  10. Houngbo, K.H.: Investigating citation linkage between research articles. Ph.D. thesis, The University of Western Ontario (2017)

    Google Scholar 

  11. Huijsmans, C.J., Damen, J., van der Linden, J.C., Savelkoul, P.H., Hermans, M.H.: Comparative analysis of four methods to extract DNA from paraffin-embedded tissues: effect on downstream molecular applications. BMC Res. Notes 3(1), 239 (2010)

    Article  Google Scholar 

  12. Kayser, K., Stute, H., Lübcke, J., Wazinski, U.: Rapid microwave fixation–a comparative morphometric study. Histochem. J. 20(6–7), 347–352 (1988). https://doi.org/10.1007/BF01002728

    Article  Google Scholar 

  13. Kiros, R., et al.: Skip-thought vectors. In: Advances in Neural Information Processing Systems, pp. 3294–3302 (2015)

    Google Scholar 

  14. Li, L., et al.: Computational linguistics literature and citations oriented citation linkage, classification and summarization. Int. J. Digit. Libr. 19(2), 173–190 (2017). https://doi.org/10.1007/s00799-017-0219-5

    Article  MathSciNet  Google Scholar 

  15. Li, L., Zhang, Y., Mao, L., Chi, J., Chen, M., Huang, Z.: CIST@CLSciSumm-17: multiple features based citation linkage, classification and summarization. In: BIRNDL@ SIGIR (2), pp. 43–54 (2017)

    Google Scholar 

  16. Li, L., et al.: CIST@CLSciSumm-19: automatic scientific paper summarization with citances and facets. In: BIRNDL 2019 (2019)

    Google Scholar 

  17. Liu, Y., Sun, C., Lin, L., Wang, X.: Learning natural language inference using bidirectional LSTM model and inner-attention. arXiv preprint arXiv:1605.09090 (2016)

  18. Logeswaran, L., Lee, H.: An efficient framework for learning sentence representations. arXiv preprint arXiv:1803.02893 (2018)

  19. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  20. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  21. Neculoiu, P., Versteegh, M., Rotaru, M.: Learning text similarity with Siamese recurrent networks. In: Proceedings of the 1st Workshop on Representation Learning for NLP, pp. 148–157 (2016)

    Google Scholar 

  22. Pagliardini, M., Gupta, P., Jaggi, M.: Unsupervised learning of sentence embeddings using compositional n-Gram features. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 528–540 (2018)

    Google Scholar 

  23. Pakhomov, S., McInnes, B., Adam, T., Liu, Y., Pedersen, T., Melton, G.B.: Semantic similarity and relatedness between clinical terms: an experimental study. In: AMIA Annual Symposium Proceedings 2010, pp. 572–576 (2010)

    Google Scholar 

  24. Palau, R.M., Moens, M.F.: Argumentation mining: the detection, classification and structure of arguments in text. In: Proceedings of the 12th International Conference on Artificial Intelligence and Law, pp. 98–107. ACM (2009)

    Google Scholar 

  25. Radev, D.R., Jing, H., Budzikowska, M.: Centroid-based summarization of multiple documents: Sentence extraction, utility-based evaluation, and user studies. In: NAACL-ANLP 2000 Workshop: Automatic Summarization (2000)

    Google Scholar 

  26. Wang, Y., et al.: High quality copy number and genotype data from FFPE samples using molecular inversion probe (MIP) microarrays. BMC Med. Genomics 2(1), 8 (2009)

    Article  MathSciNet  Google Scholar 

  27. Wolffs, P., Grage, H., Hagberg, O., Rådström, P.: Impact of DNA polymerases and their buffer systems on quantitative real-time PCR. J. Clin. Microbiol. 42(1), 408–411 (2004)

    Article  Google Scholar 

  28. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)

    Google Scholar 

  29. Zhao, H., Lu, Z., Poupart, P.: Self-adaptive hierarchical sentence model. In: Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 4069–4076 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Western Ontario, London, ON, Canada

    Sudipta Singha Roy, Robert E. Mercer & Felipe Urra

Authors
  1. Sudipta Singha Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert E. Mercer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Felipe Urra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sudipta Singha Roy .

Editor information

Editors and Affiliations

  1. National Research Council Canada, Ottawa, ON, Canada

    Cyril Goutte

  2. Queen’s University, Kingston, ON, Canada

    Xiaodan Zhu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singha Roy, S., Mercer, R.E., Urra, F. (2020). Investigating Citation Linkage as a Sentence Similarity Measurement Task Using Deep Learning. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-47358-7_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-47357-0

  • Online ISBN: 978-3-030-47358-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation