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Deep context of citations using machine-learning models in scholarly full-text articles

Abstract

Information retrieval systems for scholarly literature rely heavily not only on text matching but on semantic- and context-based features. Readers nowadays are deeply interested in how important an article is, its purpose and how influential it is in follow-up research work. Numerous techniques to tap the power of machine learning and artificial intelligence have been developed to enhance retrieval of the most influential scientific literature. In this paper, we compare and improve on four existing state-of-the-art techniques designed to identify influential citations. We consider 450 citations from the Association for Computational Linguistics corpus, classified by experts as either important or unimportant, and further extract 64 features based on the methodology of four state-of-the-art techniques. We apply the Extra-Trees classifier to select 29 best features and apply the Random Forest and Support Vector Machine classifiers to all selected techniques. Using the Random Forest classifier, our supervised model improves on the state-of-the-art method by 11.25%, with 89% Precision-Recall area under the curve. Finally, we present our deep-learning model, the Long Short-Term Memory network, that uses all 64 features to distinguish important and unimportant citations with 92.57% accuracy.

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Notes

  1. 1.

    http://allenai.org/data.html.

  2. 2.

    https://nlp.stanford.edu/software/lex-parser.shtml.

  3. 3.

    http://opennlp.apache.org/.

  4. 4.

    http://parscit.comp.nus.edu.sg.

References

  1. Abadi, M., & TensorFlow, A. A. B. P. (2016). Large-scale machine learning on heterogeneous distributed systems. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16), Savannah, GA, USA (pp. 265–283).

  2. Abu-Jbara, A., Ezra, J., & Radev, D. (2013). Purpose and polarity of citation: Towards nlp-based bibliometrics. In Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: Human language technologies (pp. 596–606).

  3. Agarwal, S., Choubey, L., & Yu, H. (2010). Automatically classifying the role of citations in biomedical articles. In AMIA Annual Symposium Proceedings (Vol. 2010, p. 11). American Medical Informatics Association.

  4. Athar, A. (2011, June). Sentiment analysis of citations using sentence structure-based features. In Proceedings of the ACL 2011 student session (pp. 81–87). Association for Computational Linguistics.

  5. Auria, L., & Moro, R. A. (2008). Support vector machines (SVM) as a technique for solvency analysis. Technical report, Deutsche Bundesbank, Hannover; German Institute for Economic Research, Berlin. (2007)

  6. Balaban, A. T. (2012). Positive and negative aspects of citation indices and journal impact factors. Scientometrics, 92(2), 241–247.

    Article  Google Scholar 

  7. Bertin, M., & Atanassova, I. (2018). The context of multiple in-text references and their signification. International Journal on Digital Libraries, 19(2-3), 287-303.

    Google Scholar 

  8. Bett, M., Gross, R., Yu, H., Zhu, X., Pan, Y., Yang, J., & Waibel, A. (2000). Multimodal meeting tracker. In Content-Based Multimedia Information Access (Vol. 1, pp. 32–45).

  9. Borgman, C. L. (1990). Scholarly communication and bibliometrics. Newbury Park: Sage Publications.

    Google Scholar 

  10. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    Article  Google Scholar 

  11. Cao, H., Naito, T., & Ninomiya, Y. (2008, October). Approximate RBF kernel SVM and its applications in pedestrian classification. In The 1st International Workshop on Machine Learning for Vision-based Motion Analysis-MLVMA’08.

  12. Chubin, D. E., & Moitra, S. D. (1975). Content analysis of references: Adjunct or alternative to citation counting? Social Studies of Science, 5(4), 423–441.

    Article  Google Scholar 

  13. Cohan, A., & Goharian, N. (2017). Scientific document summarization via citation contextualization and scientific discourse. International Journal on Digital Libraries, 19(2–3), 287-303.

    Google Scholar 

  14. Conrad, J. G., & Dabney, D. P. (2001, October). Automatic recognition of distinguishing negative indirect history language in judicial opinions. In Proceedings of the tenth international conference on Information and knowledge management (pp. 287–294). ACM.

  15. De Vocht, L., Softic, S., Verborgh, R., Mannens, E., & Ebner, M. (2017). Social semantic search: a case study on web 2.0 for science. International Journal on Semantic Web and Information Systems, 13(4), 155–180.

    Article  Google Scholar 

  16. Di Ciaccio, A., & Giorgi, G. M. (2015). Deep learning for supervised classification. Rivista Italiana di Economia Demografia e Statistica, 69(2), 2–10.

    Google Scholar 

  17. Ding, Y., Zhang, G., Chambers, T., Song, M., Wang, X., & Zhai, C. (2014). Content-based citation analysis: The next generation of citation analysis. Journal of the Association for Information Science and Technology, 65(9), 1820–1833.

    Article  Google Scholar 

  18. Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.

    MathSciNet  Article  Google Scholar 

  19. Finney, B. (1979). The reference characteristics of scientific texts. Doctoral dissertation, City University (London, England).

  20. Frost, C. O. (1979). The use of citations in literary research: A preliminary classification of citation functions. The Library Quarterly, 49(4), 399–414.

    Article  Google Scholar 

  21. Garfield, E. (1965, December). Can citation indexing be automated. In Statistical association methods for mechanized documentation, symposium proceedings (Vol. 269, pp. 189–192). Washington, DC: National Bureau of Standards, Miscellaneous Publication 269.

  22. Garfield, E. (2006). The history and meaning of the journal impact factor. The Journal of the American Medical Association, 295(1), 90–93.

    Article  Google Scholar 

  23. Garzone, M., & Mercer, R. (2000). Towards an automated citation classifier. In Conference of the Canadian Society for Computational Studies of Intelligence (pp. 337-346). Springer, Berlin.

  24. Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63(1), 3–42.

    Article  Google Scholar 

  25. Hassan, S. U., Akram, A., & Haddawy, P. (2017). Identifying important citations using contextual information from full text. In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), (pp. 1–8). IEEE.

  26. Hassan, S. U., Imran, M., Iftikhar, T., Safder, I., & Shabbir, M. (2017). Deep stylometry and lexical & syntactic features based author attribution on PLoS digital repository. In International Conference on Asian Digital Libraries (pp. 119–127). Springer, Cham.

  27. Hassan, S. U., Iqbal, S., Imran, M., Aljohani, N. R., & Nawaz, R. (2018). Mining the context of citations in scientific publications. In International Conference on Asian Digital Libraries (in-press). Springer, Cham.

  28. Hassan, S. U., Safder, I., Akram, A., & Kamiran, F. (2018b). A novel machine-learning approach to measuring scientific knowledge flows using citation context analysis. Scientometrics, 116(2), 973–996.

    Article  Google Scholar 

  29. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569.

    Article  Google Scholar 

  30. Hirsch, J. E. (2010a). An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics, 85(3), 741–754.

    Article  Google Scholar 

  31. Hirsch, J. E. (2010b). An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics, 85(3), 741–754.

    Article  Google Scholar 

  32. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

    Article  Google Scholar 

  33. Hoffmann, A., & Pham, S. B. (2003, October). Towards topic-based summarization for interactive document viewing. In Proceedings of the 2nd international conference on Knowledge capture (pp. 28–35). ACM.

  34. Hou, W. R., Li, M., & Niu, D. K. (2011). Counting citations in texts rather than reference lists to improve the accuracy of assessing scientific contribution. BioEssays, 33(10), 724–727.

    Article  Google Scholar 

  35. Jiang, Y., & Yang, M. (2018). Semantic search exploiting formal concept analysis, rough sets, and Wikipedia. International Journal on Semantic Web and Information Systems (IJSWIS), 14(3), 99–119.

    Article  Google Scholar 

  36. Lindsey, D. (1989). Using citation counts as a measure of quality in science measuring what’s measurable rather than what’s valid. Scientometrics, 15(3–4), 189–203.

    Article  Google Scholar 

  37. Luukkonen, T. (1992). Is scientists’ publishing behaviour rewards eeking? Scientometrics, 24(2), 297–319.

    Article  Google Scholar 

  38. Moravcsik, M. J., & Murugesan, P. (1975). Some results on the function and quality of citations. Social Studies of Science, 5(1), 86–92.

    Article  Google Scholar 

  39. Nakov, P. I., Schwartz, A. S., & Hearst, M. (2004). Citances: Citation sentences for semantic analysis of bioscience text. In Proceedings of the SIGIR (Vol. 4, pp. 81–88).

  40. Nanba, H., & Okumura, M. (1999, July). Towards multi-paper summarization using reference information. In IJCAI (Vol. 99, pp. 926-931).

  41. Oppenheim, C., & Renn, S. P. (1978). Highly cited old papers and the reasons why they continue to be cited. Journal of the Association for Information Science and Technology, 29(5), 225–231.

    Google Scholar 

  42. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends ® in Information Retrieval, 2(1–2), 1–135.

  43. Peritz, B. (1983). A classification of citation roles for the social sciences and related fields. Scientometrics, 5(5), 303–312.

    Article  Google Scholar 

  44. Pride, D., & Knoth, P. (2017, September). Incidental or influential? Challenges in automatically detecting citation importance using publication full texts. In International conference on theory and practice of digital Libraries (pp. 572–578). Springer, Cham.

  45. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85–117.

    Article  Google Scholar 

  46. Shardlow, M., Batista-Navarro, R., Thompson, P., Nawaz, R., McNaught, J., & Ananiadou, S. (2018). Identification of research hypotheses and new knowledge from scientific literature. BMC Medical Informatics and Decision Making, 18(1), 46.

    Article  Google Scholar 

  47. Small, H., & Greenlee, E. (1980). Citation context analysis of a co-citation cluster: Recombinant-DNA. Scientometrics, 2(4), 277–301.

    Article  Google Scholar 

  48. Taşkın, Z., & Al, U. (2018). A content-based citation analysis study based on text categorization. Scientometrics, 114(1), 335-357.

    Article  Google Scholar 

  49. Teufel, S., Siddharthan, A., & Tidhar, D. (2006, July). 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.

  50. Thompson, P., Nawaz, R., McNaught, J., & Ananiadou, S. (2011). Enriching a biomedical event corpus with meta-knowledge annotation. BMC Bioinformatics, 12(1), 393.

    Article  Google Scholar 

  51. Valenzuela, M., Ha, V., & Etzioni, O. (2015, April). Identifying meaningful citations. In AAAI Workshop: Scholarly Big Data.

  52. Waltman, L., van Eck, N. J., van Leeuwen, T. N., & Visser, M. S. (2013). Some modifications to the SNIP journal impact indicator. Journal of Informetrics, 7(2), 272–285.

    Article  Google Scholar 

  53. Xu, H., Martin, E., & Mahidadia, A. (2013). Using heterogeneous features for scientific citation classification. In Proceedings of the 13th Conference of the Pacific Association for Computational Linguistics.

  54. Zhang, P., & Koppaka, L. (2007, June). Semantics-based legal citation network. In Proceedings of the 11th International Conference on Artificial Intelligence and Law (pp. 123–130). ACM.

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Correspondence to Saeed-Ul Hassan.

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Appendix

See Table 13.

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Hassan, SU., Imran, M., Iqbal, S. et al. Deep context of citations using machine-learning models in scholarly full-text articles. Scientometrics 117, 1645–1662 (2018). https://doi.org/10.1007/s11192-018-2944-y

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Keywords

  • Citation-context analysis
  • Deep learning
  • Influential citations
  • Machine learning