Advertisement

Predicting Concept-Based Research Trends with Rhetorical Framing

  • Jifan Yu
  • Liangming Pan
  • Juanzi Li
  • Xiaoping Du
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 957)

Abstract

Applying data mining techniques to help researchers discover, understand, and predict research trends is a highly beneficial but challenging task. The existing researches mainly use topics extracted from literatures as objects to build predicting model. To get more accurate results, we use concepts instead of topics constructing a model to predict their rise and fall trends, considering the rhetorical characteristics of them. The experimental results based on ACL1965-2017 literature dataset show the clues of the scientific trends can be found in the rhetorical distribution of concepts. After adding the relevant concepts’ information, the predict model’s accuracy rate can be significantly improved, compared to the prior topic-based algorithm.

Keywords

Scientific trends analysis Concept extraction Scientific discourse analysis 

Notes

Acknowledgement

The work is supported by National Key Research and Development Program of China (2017YFB1002101), NSFC key project (U1736204, 61661146007) and THUNUS NExT Co-Lab.

References

  1. 1.
    Liu, Z., Huang, W., Zheng, Y., Sun, M.: Automatic keyphrase extraction via topic decomposition. In: Conference on Empirical Methods in Natural Language Processing, pp. 366–376, Association for Computational Linguistics (2010)Google Scholar
  2. 2.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. USA 101(Suppl. 1), 5228–5235 (2004)CrossRefGoogle Scholar
  3. 3.
    Anderson, A., Dan, M.F., Dan, J.: Towards a Computational History of the ACL: 1980–2008. In: ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries, pp. 13–21 (2013)Google Scholar
  4. 4.
    Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the International Conference on Machine Learning, pp. 113–120 (2006)Google Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J Mach. Learn. Res. Arch. 3, 993–1022 (2003)zbMATHGoogle Scholar
  6. 6.
    Hall, D., Jurafsky, D., Manning, C.D.: Studying the history of ideas using topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, 25–27 October 2008, Honolulu, Hawaii, USA, A Meeting of Sigdat, A Special Interest Group of the ACL, pp. 363–371. DBLP (2008)Google Scholar
  7. 7.
    Shibata, N., Kajikawa, Y., Takeda, Y., Matsushima, K.: Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation 28(11), 758–775 (2008)CrossRefGoogle Scholar
  8. 8.
    Shibata, N., Kajikawa, Y., Takeda, Y., Matsushima, K.: Comparative study on methods of detecting research fronts using different types of citation. J. Assoc. Inf. Sci. Technol. 60(3), 571–580 (2009)CrossRefGoogle Scholar
  9. 9.
    Mane, K.K., Börner, K.: Mapping topics and topic bursts in PNAS. Proc. Natl. Acad. Sci. USA 101(Suppl 1), 5287–5290 (2004)CrossRefGoogle Scholar
  10. 10.
    Guo, H., Weingart, S., Brner, K.: Mixed-indicators model for identifying emerging research areas. Scientometrics 89(1), 421–435 (2011)CrossRefGoogle Scholar
  11. 11.
    Small, H.: Tracking and predicting growth areas in science. Scientometrics 68(3), 595–610 (2006)CrossRefGoogle Scholar
  12. 12.
    Small, H.: Interpreting maps of science using citation context sentiments: a preliminary investigation. Scientometrics 87(2), 373–388 (2011)CrossRefGoogle Scholar
  13. 13.
    Prabhakaran, V., Hamilton, W.L., Dan, M.F., Dan, J.: Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing. In: Meeting of the Association for Computational Linguistics, pp. 1170–1180 (2016)Google Scholar
  14. 14.
    Grineva, M., Grinev, M., Lizorkin, D.: Extracting key terms from noisy and multitheme documents. In: International Conference on World Wide Web, WWW 2009, Madrid, Spain, April, pp. 661–670. DBLP (2009)Google Scholar
  15. 15.
    Litvak, M., Last, M.: Graph-based keyword extraction for single-document summarization. In: MMIES 08 Workshop on Multi-source Multilingual Information Extraction & Summar, vol. 64, pp. 17–24 (2008)Google Scholar
  16. 16.
    Liu, Z., Li, P., Zheng, Y., Sun, M.: Clustering to find exemplar terms for keyphrase extraction. In: Conference on Empirical Methods in Natural Language Processing, vol. 1, PP. 257–266 (2009)Google Scholar
  17. 17.
    Turney, P.D.: Learning algorithms for keyphrase extraction. Inf. Retrieval 2(4), 303–336 (2000)CrossRefGoogle Scholar
  18. 18.
    Wan, X., Xiao, J.: Single document keyphrase extraction using neighborhood knowledge. In: National Conference on Artificial Intelligence, pp. 855–860. AAAI Press (2008)Google Scholar
  19. 19.
    Yuan, J., Gao, F., Ho, Q., Dai, W., Wei, J., Zheng, X., et al.: LightLDA: big topic models on modest computer clusters. 1351–1361 (2014)Google Scholar
  20. 20.
    Wang, X., Mccallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424-433. ACM (2006)Google Scholar
  21. 21.
    Turney, P.D.: Learning to extract keyphrases from text. cs.lg/0212013(cs.LG/0212013) (2002)Google Scholar
  22. 22.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013)Google Scholar
  23. 23.
    Teufel, S.: Argumentative zoning: information extraction from scientific text (1999)Google Scholar
  24. 24.
    Liakata, M.: Zones of conceptualisation in scientific papers: a window to negative and speculative statements. In: The Workshop on Negation and Speculation in Natural Language Processing, pp. 1-4. Association for Computational Linguistics (2010)Google Scholar
  25. 25.
    Nguyen, T.D., Kan, M.-Y.: Keyphrase extraction in scientific publications. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 317–326. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-77094-7_41CrossRefGoogle Scholar
  26. 26.
    Mihalcea, R.: Textrank: bringing order into texts. In: EMNLP, pp. 404–411 (2004)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jifan Yu
    • 1
  • Liangming Pan
    • 2
  • Juanzi Li
    • 1
  • Xiaoping Du
    • 3
  1. 1.Tsinghua UniversityBeijingChina
  2. 2.National University of SingaporeSingaporeSingapore
  3. 3.Beihang UniversityBeijingChina

Personalised recommendations