Advertisement

Citation Classification Using Multitask Convolutional Neural Network Model

  • Abdallah Yousif
  • Zhendong Niu
  • Ally S. Nyamawe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)

Abstract

In the recent years, there has been an increased availability of scientific publications across the world connected through citations. To help analyze this huge amount of information, citation classification has been introduced to identify the opinions and purposes of the authors for citing earlier works. Existing approaches utilize machine learning techniques and report promising results in identifying the sentiment and purpose of the citations. However, most of the previous approaches tackle the citation sentiments and purposes classification in isolation. Moreover, they suffer from limited training data and time-consuming feature engineering process. In this paper, we address these issues by building a multitask learning model based on convolutional neural network. The proposed model jointly learns the citation sentiment classification (primary task) with the citation purpose classification as a related task to boost the classification performance. Experimental results on two public datasets show that our model outperforms the previous baseline methods and prove the effectiveness of multitask learning technique.

Keywords

Citation sentiment Citation purpose Convolution neural networks Multitask learning Citation classification 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61370137), the National Basic Research Program of China (No. 2012CB7207002), the Ministry of Education - China Mobile Research Foundation Project No. 2016/2-7 and the 111 Project of Beijing Institute of Technology.

References

  1. 1.
    Alvarez, M.H., Gómez, J.M.: Survey in sentiment, polarity and function analysis of citation. In: Proceedings of the First Workshop on Argumentation Mining, pp. 102–103. Association for Computational Linguistics (ACL) (2014)Google Scholar
  2. 2.
    Yousif, A., Niu, Z., Tarus, J.K., Ahmad, A.: A survey on sentiment analysis of scientific citations. Artif. Intell. Rev. (2017)Google Scholar
  3. 3.
    Garfield, E.: Citation analysis as a tool in journal evaluation. Journals can be ranked by frequency and impact of citations for science policy studies. Science 178, 471–479 (1972)CrossRefGoogle Scholar
  4. 4.
    Teufel, S., Siddharthan, A., Tidhar, D.: Automatic classification of citation function. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 103–110. Association for Computational Linguistics, 1610091 (2006)Google Scholar
  5. 5.
    Waltman, L.: A review of the literature on citation impact indicators. J. Informetric. 10, 365–391 (2016)CrossRefGoogle Scholar
  6. 6.
    Abu-Jbara, A., Ezra, J., Radev, D.R.: Purpose and polarity of citation: towards NLP-based bibliometrics. In: Proceedings of the North American Association for Computational Linguistics (NAACL-HLT 2013), pp. 596–606. Association for Computational Linguistics: Human Language Technologies (2013)Google Scholar
  7. 7.
    Moravcsik, M.J., Murugesan, P.: Some results on the function and quality of citations. Soc. Stud. Sci. 5, 86–92 (1975)CrossRefGoogle Scholar
  8. 8.
    Garfield, E.: Can citation indexing be automated. In: Statistical Association Methods for Mechanized Documentation, Symposium Proceedings, pp. 189–192. National Bureau of Standards, Miscellaneous Publication 269, Washington, DC (1965)Google Scholar
  9. 9.
    Teufel, S., Carletta, J., Moens, M.: An annotation scheme for discourse-level argumentation in research articles. In: Proceedings of the Ninth Conference on European chapter of the Association for Computational Linguistics, pp. 110–117. Association for Computational Linguistics (1999)Google Scholar
  10. 10.
    Athar, A.: Sentiment analysis of citations using sentence structure-based features. In: Proceedings of the ACL 2011 Student Session, pp. 81–87. Association for Computational Linguistics, 2000991 (2011)Google Scholar
  11. 11.
    Dong, C., Schäfer, U.: Ensemble-style self-training on citation classification. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, pp. 623–631. Association for Computational Linguistics (ACL) (2011)Google Scholar
  12. 12.
    Hernandez-Alvarez, M., Gomez S, J.M.: Citation impact categorization: for scientific literature. In: Ferreira, J.C. (ed.) IEEE 18th International Conference on Computational Science and Engineering (CSE), 21–23 October 2015, pp. 307–313. IEEE Computer Society, Los Alamitos (2015)Google Scholar
  13. 13.
    Jochim, C., Schutze, H.: Improving citation polarity classification with product reviews. In: Marcu, D. (ed.) 52nd Annual Meeting of the Association for Computational Linguistics, ACL vol. 2, pp. 42–48. Association for Computational Linguistics (ACL), Baltimore (2014)Google Scholar
  14. 14.
    Sula, C.A., Miller, M.: Citations, contexts, and humanistic discourse: toward automatic extraction and classification. LLC 29, 452–464 (2014)Google Scholar
  15. 15.
    Balikas, G., Moura, S., Amini, M.-R.: Multitask learning for fine-grained twitter sentiment analysis. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, pp. 1005–1008. ACM (2017)Google Scholar
  16. 16.
    Li, X., He, Y., Meyers, A., Grishman, R.: Towards fine-grained citation function classification, pp. 402–407 (2013)Google Scholar
  17. 17.
    Kim, Y.: Convolutional neural networks for sentence classification. In: 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25 October 2014–29 October 2014, pp. 1746–1751. Association for Computational Linguistics (ACL) (2014)Google Scholar
  18. 18.
    Noushahr, H.G., Ahmadi, S.: Multitask learning for text classification with deep neural networks. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXXIII, pp. 119–133. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47175-4_8CrossRefGoogle Scholar
  19. 19.
    Radev, D.R., Muthukrishnan, P., Qazvinian, V.: The ACL anthology network corpus. In: Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries, pp. 54–61. Association for Computational Linguistics, 1699759 (2009)Google Scholar
  20. 20.
    Athar, A., Teufel, S.: Detection of implicit citations for sentiment detection. In: Proceedings of the Workshop on Detecting Structure in Scholarly Discourse, pp. 18–26. Association for Computational Linguistics, 2391176 (2012)Google Scholar
  21. 21.
    Parthasarathy, G., Tomar, D.C.: Sentiment analyzer: analysis of journal citations from citation databases. In: Abhay Bansal, N.H., Singhal, A. (ed.) 5th International Conference - Confluence the Next Generation Information Technology Summit (Confluence), Noida, India, pp. 923–928. IEEE (2014)Google Scholar
  22. 22.
    In Cheol, K., Thoma, G.R.: Automated classification of author’s sentiments in citation using machine learning techniques: a preliminary study. In: Corns, S. (ed.) IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Piscataway, NJ, USA, 12–15 August 2015, pp. 1–7. IEEE (2015)Google Scholar
  23. 23.
    Xu, J., Zhang, Y., Wu, Y., Wang, J., Dong, X., Xu, H.: Citation sentiment analysis in clinical trial papers. In: AMIA Annual Symposium Proceedings 2015, pp. 1334–1341 (2015)Google Scholar
  24. 24.
    Ma, Z., Nam, J., Weihe, K.: Improve sentiment analysis of citations with author modelling. In: Proceedings of the Fifth Workshop on Computational Linguistics for Literature - NAACL-HLT 2016, pp. 122–127. Association for Computational Linguistics (ACL) (2016)Google Scholar
  25. 25.
    Lauscher, A., Glava, G., Ponzetto, S.P., Eckert, K.: Investigating convolutional networks and domain-specific embeddings for semantic classification of citations. In: Proceedings of the 6th International Workshop on Mining Scientific Publications, Toronto, ON, Canada, pp. 24–28. ACM (2017)Google Scholar
  26. 26.
    Jha, R., Jbara, A.-A., Qazvinian, V., Radev, D.R.: NLP-driven citation analysis for scientometrics. Nat. Lang. Eng. 23, 93–130 (2016)CrossRefGoogle Scholar
  27. 27.
    Hochreiter, S., Schmidhuber, R.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)Google Scholar
  28. 28.
    Athar, A., Teufel, S.: Context-enhanced citation sentiment detection. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 597–601, Montreal, Canada (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Abdallah Yousif
    • 1
  • Zhendong Niu
    • 1
    • 2
  • Ally S. Nyamawe
    • 1
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  2. 2.School of Computing and InformationUniversity of PittsburghPittsburghUSA

Personalised recommendations