Identifying Influential References in Scholarly Papers Using Citation Contexts

1. Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 3615–3620 (2019)

   Google Scholar 

2. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(1), 993–1022 (2003)

   MATH  Google Scholar 

3. de Andrade, C.M.V., Gonçalves, M.A.: Combining representations for effective citation classification. In: Proceedings of the 8th International Workshop on Mining Scientific Publications, pp. 54–58 (2020)

   Google Scholar 

4. Forney, G.: The Viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)

   Article  MathSciNet  Google Scholar 

5. Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)

   Article  Google Scholar 

6. Hutto, C., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the 8th International AAAI Conference on Web and Social Media, pp. 216–225 (2014)

   Google Scholar 

7. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference on European Chapter of the Association for Computational Linguistics, pp. 427–431 (2017)

   Google Scholar 

8. Kunnath, S.N., Pride, D., Gyawali, B., Knoth, P.: Overview of the 2020 WOSP 3C citation context classification task. In: Proceedings of the 8th International Workshop on Mining Scientific Publications, pp. 75–83 (2020)

   Google Scholar 

9. Kunnath, S.N., Pride, D., Herrmannova, D., Knoth, P.: Overview of the 2021 SDP 3C citation context classification shared task. In: Proceedings of the 2nd Workshop on Scholarly Document Processing, pp. 150–158 (2021)

   Google Scholar 

10. Maheshwari, H., Singh, B., Varma, V.: SciBERT sentence representation for citation context classification. In: Proceedings of the 2nd Workshop on Scholarly Document Processing, pp. 130–133 (2021)

    Google Scholar 

11. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Workshop Track Proceedings of the 1st International Conference on Learning Representations (2013)

    Google Scholar 

12. Mishra, S., Mishra, S.: Scubed at 3C task B - a simple baseline for citation context influence classification. In: Proceedings of the 8th International Workshop on Mining Scientific Publications, pp. 65–70 (2020)

    Google Scholar 

13. Nakayama, H., Kubo, T., Kamura, J., Taniguchi, Y., Liang, X.: doccano: text annotation tool for human (2018). https://github.com/doccano/doccano

14. Paszke, A. et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8024–8035 (2019)

    Google Scholar 

15. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)

    Google Scholar 

16. Premjith, B., Isha, I.S., Kumar, K.S., Karthikeyan, L., Soman, K.P.: Amrita_CEN_NLP@SDP2021 task A and B. In: Proceedings of the 2nd Workshop on Scholarly Document Processing, pp. 146–149 (2021)

    Google Scholar 

17. Premjith, B., Soman, K.P.: Amrita_CEN_NLP @ WOSP 3C citation context classification task. In: Proceedings of the 8th International Workshop on Mining Scientific Publications, pp. 71–74 (2020)

    Google Scholar 

18. Pride, D., Knoth, P.: Incidental or influential? - Challenges in automatically detecting citation importance using publication full texts. In: Proceedings of the 21st International Conference on Theory and Practice of Digital Libraries, pp. 572–578 (2017)

    Google Scholar 

19. Su, X., Prasad, A., Kan, M.Y., Sugiyama, K.: Neural multi-task learning for citation function and provenance. In: Proceedings of the 2019 ACM/IEEE Joint Conference on Digital Libraries, pp. 394–395 (2019)

    Google Scholar 

20. Taşkın, Z., Al, U.: A content-based citation analysis study based on text categorization. Scientometrics 114(1), 335–357 (2017). https://doi.org/10.1007/s11192-017-2560-2

    Article  Google Scholar 

21. Thomas, W. et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 38–45 (2020)

    Google Scholar 

22. Valenzuela, M., Ha, V.A., Etzioni, O.: Identifying meaningful citations. In: AAAI Workshop: Scholarly Big Data, pp. 21–26 (2015)

    Google Scholar 

23. Varanasi, K.K., Ghosal, T., Tiwary, P., Singh, M.: IITP-CUNI@3C: supervised approaches for citation classification (task A) and citation significance detection (task B). In: Proceedings of the 2nd Workshop on Scholarly Document Processing, pp. 140–145 (2021)

    Google Scholar 

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