This story draft by @escholar has not been reviewed by an editor, YET.

Ethics Statement and References

EScholar: Electronic Academic Papers for Scholars HackerNoon profile picture
0-item

Table of Links

Abstract and 1. Introduction

  1. Related Work

  2. Proposed Dataset

  3. SymTax Model

    4.1 Prefetcher

    4.2 Enricher

    4.3 Reranker

  4. Experiments and Results

  5. Analysis

    6.1 Ablation Study

    6.2 Quantitative Analysis and 6.3 Qualitative Analysis

  6. Conclusion

  7. Limitations

  8. Ethics Statement and References

Appendix

9 Ethics Statement

Our work focuses on advancing citation recommendation and assisting the researchers in their academic writing process, where we are committed to maintain ethical standards. We will release our curated dataset and it can serve as a large and suitable benchmark for future research. Upholding transparency, our methodologies adhere to ethical guidelines, ensuring the responsible considerations. We assert that our work contributes positively to the citation ecosystem without raising ethical or moral concerns. We remain vigilant in addressing any unforeseen ethical challenges, driven by a commitment to principled research conduct. Our goal is to foster collaboration, uphold privacy, and enhance scholarly discourse.

References

Zafar Ali, Guilin Qi, Khan Muhammad, Pavlos Kefalas, and Shah Khusro. 2021. Global citation recommendation employing generative adversarial network. Expert Syst. Appl., 180(C).


Iz Beltagy, Kyle Lo, and Arman Cohan. 2019. 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 (EMNLP-IJCNLP), pages 3615– 3620, Hong Kong, China. Association for Computational Linguistics.


Chandra Bhagavatula, Sergey Feldman, Russell Power, and Waleed Ammar. 2018. Content-based citation recommendation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 238–251, New Orleans, Louisiana. Association for Computational Linguistics.


Lutz Bornmann, Robin Haunschild, and Rüdiger Mutz. 2021. Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanities and Social Sciences Communications, 8(1):1–15.


Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel Weld. 2020. SPECTER: Document-level representation learning using citation-informed transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2270–2282, Online. Association for Computational Linguistics.


Tao Dai, Li Zhu, Yaxiong Wang, and Kathleen M Carley. 2019. Attentive stacked denoising autoencoder with bi-lstm for personalized context-aware citation recommendation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28:553–568.


Yao Deng, Xi Zheng, Tianyi Zhang, Chen Chen, Guannan Lou, and Miryung Kim. 2020. An analysis of adversarial attacks and defenses on autonomous driving models. In 2020 IEEE international conference on pervasive computing and communications (PerCom), pages 1–10. IEEE.


Travis Ebesu and Yi Fang. 2017. Neural citation network for context-aware citation recommendation. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pages 1093–1096.


Octavian Ganea, Gary Bécigneul, and Thomas Hofmann. 2018. Hyperbolic neural networks. Advances in neural information processing systems, 31.


Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann. 2019. Combining neural networks with personalized pagerank for classification on graphs. In International Conference on Learning Representations.


Nianlong Gu, Yingqiang Gao, and Richard HR Hahnloser. 2022. Local citation recommendation with hierarchical-attention text encoder and scibert-based reranking. In European Conference on Information Retrieval, pages 274–288. Springer.


Lantian Guo, Xiaoyan Cai, Fei Hao, Dejun Mu, Changjian Fang, and Libin Yang. 2017. Exploiting fine-grained co-authorship for personalized citation recommendation. IEEE Access, 5:12714–12725.


Qi He, Jian Pei, Daniel Kifer, Prasenjit Mitra, and Lee Giles. 2010. Context-aware citation recommendation. In Proceedings of the 19th international conference on World wide web, pages 421–430.


Wenyi Huang, Saurabh Kataria, Cornelia Caragea, Prasenjit Mitra, C Lee Giles, and Lior Rokach. 2012. Recommending citations: translating papers into references. In Proceedings of the 21st ACM international conference on Information and knowledge management, pages 1910–1914.


Wenyi Huang, Zhaohui Wu, Chen Liang, Prasenjit Mitra, and C Giles. 2015. A neural probabilistic model for context based citation recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 29.


Chanwoo Jeong, Sion Jang, Eunjeong Park, and Sungchul Choi. 2020. A context-aware citation recommendation model with bert and graph convolutional networks. Scientometrics, 124:1907–1922.


Rob Johnson, Anthony Watkinson, and Michael Mabe. 2018. The stm report. An overview of scientific and scholarly publishing. 5th edition October, page 94.


Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT, volume 1, page 2.


Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.


Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Ro{bert}a: A robustly optimized {bert} pretraining approach.


Avishay Livne, Vivek Gokuladas, Jaime Teevan, Susan T Dumais, and Eytan Adar. 2014. Citesight: supporting contextual citation recommendation using differential search. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 807–816.


Zoran Medic and Jan Šnajder. 2020. Improved local citation recommendation based on context enhanced with global information. In Proceedings of the first workshop on scholarly document processing, pages 97–103.


Laurent Meunier, Raphael Ettedgui, Rafael Pinot, Yann Chevaleyre, and Jamal Atif. 2022. Towards consistency in adversarial classification. In Advances in Neural Information Processing Systems, volume 35, pages 8538–8549. Curran Associates, Inc.


Gabriela F Nane, Nicolas Robinson-Garcia, François van Schalkwyk, and Daniel Torres-Salinas. 2023. Covid-19 and the scientific publishing system: growth, open access and scientific fields. Scientometrics, 128(1):345–362.


Malte Ostendorff, Nils Rethmeier, Isabelle Augenstein, Bela Gipp, and Georg Rehm. 2022. Neighborhood contrastive learning for scientific document representations with citation embeddings. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11670–11688, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.


Stephen Robertson, Hugo Zaragoza, et al. 2009. The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends® in Information Retrieval, 3(4):333–389.


Ramit Sawhney, Ritesh Soun, Shrey Pandit, Megh Thakkar, Sarvagya Malaviya, and Yuval Pinter. 2022. Ciaug: Equipping interpolative augmentation with curriculum learning. In NAACL, pages 1758–1764.


Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representations.


Yifan Wang, Yiping Song, Shuai Li, Chaoran Cheng, Wei Ju, Ming Zhang, and Sheng Wang. 2022. Disencite: Graph-based disentangled representation learning for context-specific citation generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 11449–11458.


Qianqian Xie, Yutao Zhu, Jimin Huang, Pan Du, and Jian-Yun Nie. 2021. Graph neural collaborative topic model for citation recommendation. ACM Transactions on Information Systems (TOIS), 40(3):1–30.


Authors:

(1) Karan Goyal, IIIT Delhi, India ([email protected]);

(2) Mayank Goel, NSUT Delhi, India ([email protected]);

(3) Vikram Goyal, IIIT Delhi, India ([email protected]);

(4) Mukesh Mohania, IIIT Delhi, India ([email protected]).


This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license.


L O A D I N G
. . . comments & more!

About Author

EScholar: Electronic Academic Papers for Scholars HackerNoon profile picture
EScholar: Electronic Academic Papers for Scholars@escholar
We publish the best academic work (that's too often lost to peer reviews & the TA's desk) to the global tech community

Topics

Around The Web...

Trending Topics

blockchaincryptocurrencyhackernoon-top-storyprogrammingsoftware-developmenttechnologystartuphackernoon-booksBitcoinbooks