Abstract
Citation plays a pivotal role in determining the associations among research articles. It portrays essential information in indicative, supportive, or contrastive studies. The task of inline citation classification aids in extrapolating these relationships; However, existing studies are still immature and demand further scrutiny. Current datasets and methods used for inline citation classification only use citation-marked sentences constraining the model to turn a blind eye to domain knowledge and neighboring contextual sentences. In this paper, we propose a new dataset, named 3Cext, which along with the cited sentences, provides discourse information using the vicinal sentences to analyze the contrasting and entailing relationships as well as domain information. We propose PeriCite, a Transformer-based deep neural network that fuses peripheral sentences and domain knowledge. Our model achieves the state-of-the-art on the 3Cext dataset by \(+0.09\) F1 against the best baseline. We conduct extensive ablations to analyze the efficacy of the proposed dataset and model fusion methods.
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References
Abu-Jbara, A., Ezra, J., Radev, D.: Purpose and polarity of citation: towards NLP-based bibliometrics. In: NAACL, pp. 596–606 (2013)
Beltagy, I., Lo, K., Cohan, A.: Scibert: a pretrained language model for scientific text. arXiv preprint arXiv:1903.10676 (2019)
Cohan, A., Ammar, W., van Zuylen, M., Cady, F.: Structural scaffolds for citation intent classification in scientific publications. In: NAACL, Minneapolis, Minnesota, pp. 3586–3596. ACL (2019)
Cohan, A., Goharian, N.: Contextualizing citations for scientific summarization using word embeddings and domain knowledge. In: ACM SIGIR, pp. 1133–1136 (2017)
Cohan, A., Soldaini, L., Goharian, N.: Matching citation text and cited spans in biomedical literature: a search-oriented approach. In: NAACL, pp. 1042–1048 (2015)
Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)
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
Hernández-Alvarez, M., Gómez, J.M.: Citation impact categorization: for scientific literature. In: 2015 IEEE ICCSE18th International Conference on Computational Science and Engineering, pp. 307–313. IEEE (2015)
Ikram, M.T., Afzal, M.T.: Aspect based citation sentiment analysis using linguistic patterns for better comprehension of scientific knowledge. Scientometrics 119(1), 73–95 (2019). https://doi.org/10.1007/s11192-019-03028-
Jurgens, D., Kumar, S., Hoover, R., McFarland, D., Jurafsky, D.: Measuring the evolution of a scientific field through citation frames. Trans. Assoc. Comput. Linguist. 6, 391–406 (2018)
Kunnath, S.N., Herrmannova, D., Pride, D., Knoth, P.: A meta-analysis of semantic classification of citations. Quant. Sci. Stud. 2(4), 1170–1215 (2022)
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. Association for Computational Linguistics (2020)
Kunnath, S.N., Pride, D., Herrmannova, D., Knoth, P.: Overview of the 2021 SDP 3C citation context classification shared task. Association for Computational Linguistics (2021)
Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)
Li, H., Wu, X.J., Durrani, T.: NestFuse: an infrared and visible image fusion architecture based on nest connection and spatial/channel attention models. IEEE Trans. Instrum. Meas. 69(12), 9645–9656 (2020)
Moravcsik, M.J., Murugesan, P.: Some results on the function and quality of citations. Soc. Stud. Sci. 5(1), 86–92 (1975)
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: EMNLP, Doha, Qatar, pp. 1532–1543. ACL (2014). https://doi.org/10.3115/v1/D14-1162. https://aclanthology.org/D14-1162/
Pham, S.B., Hoffmann, A.: A new approach for scientific citation classification using cue phrases. In: Gedeon, T.T.D., Fung, L.C.C. (eds.) AI 2003. LNCS (LNAI), vol. 2903, pp. 759–771. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-24581-0_65
Pride, D., Knoth, P., Harag, J.: Act: an annotation platform for citation typing at scale. In: ACM/IEEE JCDL, pp. 329–330. IEEE (2019)
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1–67 (2020)
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108 (2019)
Sarzynska-Wawer, J., et al.: Detecting formal thought disorder by deep contextualized word representations. Psychiatry Res. 304, 114135 (2021)
Su, X., Prasad, A., Kan, M.Y., Sugiyama, K.: Neural multi-task learning for citation function and provenance. In: 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 394–395. IEEE (2019)
Teufel, S., Siddharthan, A., Tidhar, D.: Automatic classification of citation function. In: EMNLP Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 103–110 (2006)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Vs, V., Valanarasu, J.M.J., Oza, P., Patel, V.M.: Image fusion transformer. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 3566–3570. IEEE (2022)
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The research reported in this paper is funded by Crimson AI Pvt. Ltd.
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Gupta, P., Atri, Y.K., Nagvenkar, A., Dasgupta, S., Chakraborty, T. (2023). Inline Citation Classification Using Peripheral Context and Time-Evolving Augmentation. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13938. Springer, Cham. https://doi.org/10.1007/978-3-031-33383-5_1
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