The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A context-aware citation recommendation model with BERT and graph convolutional networks
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Variational Graph Auto-EncodersPreprint
Kipf, T., & Welling, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07308 · Cited by 4,300
Learning Convolutional Neural Networks for GraphsPreprint
Niepert, M., Ahmed, M., & Kutzkov, K. (2016) · International Conference on Machine Learning · doi:10.48550/arxiv.1605.05273 · Cited by 2,315
Measuring Contextual Citation Impact of Scientific Journals
Moed, H. F. (2010) · Journal of Informetrics · doi:10.1016/j.joi.2010.01.002 · Cited by 628
Context-aware citation recommendation
He, Q., Pei, J., Kifer, D., Mitra, P., & Giles, C. L. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772734 · Cited by 371
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Kang, D., Ammar, W., Dalvi, B., Zuylen, M., Kohlmeier, S., Hovy, E., & Schwartz, R. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1149 · Cited by 270
The ACL anthology network corpus
Radev, D., Muthukrishnan, P., Qazvinian, V., & Abu-Jbara, A. (2013) · Language Resources and Evaluation · doi:10.1007/s10579-012-9211-2 · Cited by 252
A scientometric review of emerging trends and new developments in recommendation systems
Kim, M. C., & Chen, C. (2015) · Scientometrics · doi:10.1007/s11192-015-1595-5 · Cited by 182
A Neural Probabilistic Model for Context Based Citation Recommendation
Huang, W., Wu, Z., Liang, C., Mitra, P., & Giles, C. L. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9528 · Cited by 161
Research works printed but not linked: 9
- He, Q., Kifer, D., Pei, J., Mitra, P., & Giles, C. L. (2011). In Proceedings of the fourth ACM international conference on web search and data mining (pp. 755–764). ACM
- He, Q., Pei, J., Kifer, D., Mitra, P., & Giles, L. (2010). In Proceedings of the 19th international conference on World wide web (pp. 421–430). ACM
- Huang, W., Wu, Z., Liang, C., Mitra, P., & Giles, C. L. (2015). In Proceedings of the twenty-ninth AAAI conference on artificial intelligence (pp. 2404–2410). AAAI Press.
- Kang, D., Ammar, W., Dalvi, B., van Zuylen, M., Kohlmeier, S., Hovy, E., & Schwartz, R. (2018). In Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: Human language technologies, Volume 1 (Long Papers) (Vol. 1, pp. 1647–1661).
- Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv e-prints
- Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 · arXiv
- Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053 · arXiv
- Radev, D. R., Muthukrishnan, P., & Qazvinian, V. (2009). In Proceedings, ACL workshop on natural language processing and information retrieval for digital libraries. Singapore
- Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). In Proceedings of the 31st international conference on international conference on machine learning—Volume 32 (JMLR.org), ICML’14 (pp. II–1278–II–1286). http://dl.acm.org/citation.cfm?id=3044805.3045035
Cited by
Sorted by citations
Items 101–189 of 189
Desai, M., Mehta, R. G., & Rana, D. P. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-022-00345-w · Cited by 4
A Survey on Conversational Search and Applications in Biomedicine
Adatrao, N. S. K., Gadireddy, G. R., & Noh, J. (2023) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3564746.3587001 · Cited by 4
BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysis and Processing
Boukhers, Z., Mayr, P., & Peroni, S. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3469482 · Cited by 5
Aggressive Language Detection Using VGCN-BERT for Spanish Texts
Mamani-Condori, E., & Ochoa-Luna, J. E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91699-2_25 · Cited by 5
Hate Speech Detection on Social Media Using Graph Convolutional Networks
Nagar, S., Gupta, S., Bahushruth, C. S., Barbhuiya, F. A., & Dey, K. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-93413-2_1 · Cited by 4
Why Did You Not Compare with That? Identifying Papers for Use as Baselines
Bedi, M., Pandey, T., Bhatia, S., & Chakraborty, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99736-6_4 · Cited by 4
Homophily - a Driving Factor for Hate Speech on Twitter
Nagar, S., Gupta, S., Bahushruth, C. S., Barbhuiya, F. A., & Dey, K. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-93413-2_7 · Cited by 4
CiteME: Can Language Models Accurately Cite Scientific Claims?
Press, O., Hochlehnert, A., Prabhu, A., Udandarao, V., Press, O., & Bethge, M. (2024) · arXiv · doi:10.48550/arxiv.2407.12861 · Cited by 4
Analyzing the Traffic of MANETs using Graph Neural Networks
Tekdogan, T. (2022) · IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) · doi:10.1109/icmlant56191.2022.9996518 · Cited by 3
KG4CraSolver: Recommending Crash Solutions via Knowledge Graph
Du, X., Lou, Y., Liu, M., Peng, X., & Yang, T. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3616317 · Cited by 3
Enriching Language Models with Graph-Based Context Information to Better Understand Textual Data
Roethel, A., Ganzha, M., & Wróblewska, A. (2024) · Electronics · doi:10.3390/electronics13101919 · Cited by 3
Xiu, W., Johar, M. G. M., Alkawaz, M. H., & Bian, C. (2024) · Journal of Computing and Electronic Information Management · doi:10.54097/ceym1sxx · Cited by 3
Deep learning-based citation recommendation system for patentsPreprint
Choi, J., Jang, S., Kim, J., Lee, J., Yoon, J., & Choi, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10932 · Cited by 3
Self-citationWang, Q., Li, H., Ma, M., & Li, Z. (2025) · Scientometrics · doi:10.1007/s11192-025-05382-3 · Cited by 3
Khadka, A., & Sthapit, S. (2025) · Informatics · doi:10.3390/informatics12040108 · Cited by 3
Rao, V. V., Ramasamy, R. K., & Sayeed, M. S. (2025) · Future Internet · doi:10.3390/fi17120543 · Cited by 2
Liu, C., Tang, D., Lv, B., Zhang, P., & Huang, Z. (2026) · Scientometrics · doi:10.1007/s11192-025-05518-5 · Cited by 2
Mining the online infosphere: A survey
Adak, S., Chakraborty, S., Das, P., Das, M., Dash, A., Hazra, R., Mathew, B., Saha, P., Sarkar, S., & Mukherjee, A. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1453 · Cited by 3
Natural Language Controlled Real-Time Object Recognition Framework for Household Robot
Bagwe, R., Natharani, R., George, K., & Panangadan, A. V. (2021) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc51732.2021.9376044 · Cited by 2
Voice Controlled Object Grasping Robotic Arm for Visually Impaired Disabled Veterans
Natharani, R., Liri, F., Samawi, J., Lin, H., Lee, K., Ruppert, N., George, K., & Panangadan, A. V. (2021) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct52877.2021.9622354 · Cited by 2
A Survey of Literature Analysis Methods Based on Representation Learning
Zhang, Q., & Chen, Y. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-5096-4_19 · Cited by 2
Desai, M., Mehta, R. G., & Rana, D. P. (2023) · Kybernetes · doi:10.1108/k-07-2022-0998 · Cited by 2
Zhang, R., Guo, Z., & Huan, H. (2023) · Expert Systems · doi:10.1111/exsy.13511 · Cited by 2
Intermediate-Task Transfer Learning for Peer Review Score Prediction
Muangkammuen, P., Fukumoto, F., Li, J., & Suzuki, Y. (2023) · doi:10.18653/v1/2023.ijcnlp-srw.6 · Cited by 2
Personalized global citation recommendation with diversification awareness
Zhang, X., Song, S., & Xiong, Y. (2024) · Scientometrics · doi:10.1007/s11192-024-05057-5 · Cited by 2
Paper Recommendation Using SPECTER with Low-Rank and Sparse Matrix Factorization
Guo, P., Zhou, G., Lu, J., Li, Z., & Zhu, T. (2024) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2024.05.002 · Cited by 2
Degha, H. E., & Laallam, F. Z. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3680287 · Cited by 2
Bibliographic network enhanced local citation recommendation
Jiang, T., Xu, Z., Wu, C., & Zhao, D. (2025) · The Electronic Library · doi:10.1108/el-08-2024-0251 · Cited by 2
Citation Worthiness Identification for Fine-Grained Citation Recommendation Systems
Roostaee, M. (2022) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-021-00472-3 · Cited by 2
Application of Deep Learning Technologies in Serious Games
Mammadli, A., & Ismayilov, E. A. (2023) · International Conference on Problems of Cybernetics and Informatics (PCI) · doi:10.1109/pci60110.2023.10325961 · Cited by 2
Cao, D. W., Zhang, D., & Chen, H. (2019) · International Conference on Natural Language Processing and Information Retrieval (NLPIR) · doi:10.1145/3342827.3342838 · Cited by 2
Text classification based on an improved graph neural networkPreprint
Zhang, R., Guo, Z., & Huan, H. (2022) · Research Square · doi:10.21203/rs.3.rs-2385115/v1 · Cited by 2
A Multidisciplinary Multimodal Aligned Dataset for Academic Data Processing
Song, H., Xu, H., Wang, Z., Wang, Y., & Li, J. (2025) · Scientific Data · doi:10.1038/s41597-025-04415-z · Cited by 6
Sentiment Analysis using the Relationship between Users and Products
Kertkeidkachorn, N., & Shirai, K. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.547 · Cited by 1
Content- and Topology-Aware Representation Learning for Scientific Multi-Literature
Zhang, K., Song, K., Kang, Y., & Liu, X. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.465 · Cited by 3
Variational Autoencoders for Spatio-Temporal Dynamic Graph Data
Peng, Y., & Xi, S. (2024) · International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR) · doi:10.1145/3718751.3718757 · Cited by 1
Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence
Song, N., Yang, J., Fu, X., Yang, X., Xie, Y., & Wang, S. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020005 · Cited by 1
SPECTER-BS: effective citation recommendation using SPECTER with bibliographic scoring
Nguyen, S. N., Long, N. H., Dinh, T. N., Pham, P., & Vo, B. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02677-y · Cited by 1
Citation Recommendation Using Deep Learning Approach
Pillai, R. S., & Deepthi, L. R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-5221-0_36 · Cited by 1
Identifying and Representing Knowledge Delta in Scientific Literature
El-Ebshihy, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28241-6_49 · Cited by 1
Using Noise and External Knowledge to Enhance Chinese Pre-trained Model
Ma, H., Li, Z., & Guo, H. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00076 · Cited by 1
Yang, Y., Chang, C., Wu, S.-J., & Ho, C.-L. (2023) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-taiwan58799.2023.10226813 · Cited by 1
Publication recommendation in incomplete networks based on graph learning
Liu, J., & Zhang, J. (2025) · Scientometrics · doi:10.1007/s11192-024-05219-5 · Cited by 1
Citation Recommendation with Random Walking
Dang, Q.-V. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-66093-2_4 · Cited by 1
Joint Embedding Multiple Feature and Rule for Paper Recommendation
Li, W., Xie, Y., & Sun, Y. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-4549-6_5 · Cited by 1
Text Classification Method Based on PEGCNPreprint
Guo, Z., Zhang, R., & Huan, H. (2023) · doi:10.22541/au.168210369.91223406/v1 · Cited by 1
Aspect-based Document Similarity for Literature Recommender Systems
Ostendorff, M. (2023) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-9937 · Cited by 1
Niimi, J. (2025) · arXiv · doi:10.48550/arxiv.2510.25378 · Cited by 7
“In the context of citation recommendation [24, 25, 26], this implies that frequently cited papers which appear across numerous publications and other web sources are more likely to be verbatimly recalled by LLMs, whereas sparsely represented works tend to be fabricated.”
Heterogeneous Graph Collaborative Filtering using Textual InformationPreprint
Wang, C., Guo, Z., Li, G., Li, J., Pan, P., & Liu, K. (2020) · arXiv (Cornell University)
Li, K. (2021) · Online Information Review · doi:10.1108/oir-03-2021-0171
Graph-Enhanced Document Representation for Court Case Retrieval
Fink, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99739-7_59
Analyzing the usage of source texts in new documents
MacLaughlin, A. (2021) · Digital Repository Service (Northeastern University) · doi:10.17760/d20406226
Research of Sentiment Analysis Model Fusing Local and Global Information
Wang, X., & Li, X. (2022) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp54964.2022.9778653
Bouhandi, M., Morin, E., & Thierry Hamon, C. (2022) · doi:10.18653/v1/2022.dlg4nlp-1.5
GNN-based retrieval and recommadation system: A semantic enhenced graph model
Wang, L., Li, X., Zhang, H., Dai, Y. F., & Zhang, S. (2022) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec55388.2022.10019945
Smart Intelligent System that can Code Like a Human Being
Shah, J., Jain, R., Jolly, V., & Kalbande, D. R. (2023) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet57972.2023.10169940
Citation Recommendation Chatbot for Professional Communities
Neumann, A. T., Słupczyński, M., Yin, Y., Li, C., & Decker, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42141-9_4
Protasiewicz, J. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-32696-7_2
Improving Peer-Review Score Prediction via Pretrained Model with Intermediate-Task Training
Muangkammuen, P., Fukumoto, F., Li, J., & Suzuki, Y. (2024) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom60618.2024.10418356
BERT-Enhanced Graph Convolutional Network for News Text Classification
Li, L., & Geng, Y. (2023) · IEEE Smart World Congress (SWC) · doi:10.1109/swc57546.2023.10448791
ICRM: An intelligent citation recommendation mechanism based on BERT and weighted BoW models
Chang, C., Yang, Y., Zhang, Q., Lin, Y.-T., & Roy, D. S. (2024) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-237975
MCARS-CC: A Scalable Multi-context-aware Recommender System
Nawara, D. (2024) · doi:10.32920/25413835
Multi-level Learning for Semi-supervised Multi-view Classification
Song, N., Yang, S., Cai, Z., Lin, J., & Huang, S. (2023) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd63341.2023.00034
A Comment Aspect-Level User Preference Transfer Model for Cross-Domain Recommendations
Zhang, W., Zhang, J., & Zhang, Y. (2024) · Information Resources Management Journal · doi:10.4018/irmj.345360
Wei, X. (2024) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2024-3693
Social Network-Based Academic Recommendation
Wang, S., Jiang, S., & Du, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-8705-0_15
Benchmark Creation for Narrative Knowledge Delta Extraction Tasks: Can LLMs Help?
El-Ebshihy, A., Ningtyas, A. M., Piroi, F., & Rauber, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88714-7_32
A Parallel Corpus of Chinese-English Legal Judgments with Argumentative Structure Annotations
Zhou, J., Cheng, F., Zhang, X., & Huang, K. (2025) · Data Intelligence · doi:10.3724/2096-7004.di.2025.0056
Citation Classification Based on Formal Concept-Enhanced Graph Convolutional Network
Wu, K., Zhu, X., Hao, F., Zhang, X., & Wang, H. (2024) · International Conference on Ubiquitous Computing and Communications (IUCC) · doi:10.1109/iucc65928.2024.00035
Nurjahan, V. A., & Jancy, S. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70224
Graph-Based Filtering Using Community Selection for Citation Recommendation
Hadhiatma, A. (2025) · Studies in computational intelligence · doi:10.1007/978-3-032-01133-6_14
Identification of Potential Co-citation Linkages via Context-Aware Citation Network Embeddings
Eto, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-06136-2_20
Jang, Y., Lee, J., Jeong, K., & Kim, J. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-025-01140-y
Reference Recommendation for Large Language Models-Generated Text Using Deep Textual Representations
Bashir, S. (2026) · Romanian Journal of Information Science and Technology · doi:10.59277/romjist.2026.1.01
Context aware deep learning techniques for analysis of breast cancer histopathology: a review
Dutta, R. M., Sekh, A. A., Dogra, D. P., & Dan, P. K. (2026) · Discover Computing · doi:10.1007/s10791-026-10071-7
Local citation relationship classification for recommendation: a SciBERT-based DualFocusNet approach
Lu, Y., Yuan, M., & Chen, M. (2026) · Scientometrics · doi:10.1007/s11192-026-05627-9
Experimental Evaluation of Related Papers Finding Techniques
Idrees, M. A., Qadir, M. A., & Nageen, M. (2026) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1790
Chatelain, I. B. C., Su, P., & Thierry, N. (2026) · Scientometrics · doi:10.1007/s11192-026-05755-2
Citation Intent Classification via Exponential Borda Fusion and SciBERT
Barchane, M., Belefqih, S., Benlahmar, E. H., Zahour, O., & Zellou, A. (2026) · Algorithms · doi:10.3390/a19080612
Vadisena, V. K. R., & Vaithianathan, G. (2026) · Journal of Systems Science and Systems Engineering · doi:10.1007/s11518-026-5730-z
Gen-AI Interview Simulator & Resume Advisor for Top IT Firms
Jayamma, M., Venkataiah, C., B.Supraja, G.Kowsalya, A.Jyothi, & G.Chennakeswari (2026) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss69635.2026.11645678
DeepSeek-Assisted Dynamic Impact Monitoring System for Industrial Chain
Tang, H., Yuan, Y., & Gong, T. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10090302
Biancofiore, G. M., Palma, D. D., Pomo, C., Boratto, L., Noia, T. D., & Narducci, F. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.101058
HQA-Rec: Hyperbolic Quantization Alignment for Citation Recommendation
Liu, Y., Du, Y., Nguyen, T.-S., & Cambria, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38410-2_40
Instant local citation recommendation via non-sampling embedding of dynamic historical citations
Li, H., & Zhang, J. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135125
Knowledge Graph-Based Approaches in Scholarly Recommender Systems: A Comprehensive Analysis
Ovono, G., Zuva, T., & Otunniyi, T. O. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-38815-5_26
Zhang, J., Li, H., & Liu, D. (2026) · Scientometrics · doi:10.1007/s11192-026-05875-9
Metadata
- Authors
- Jeong, C., Jang, S., Park, E., & Choi, S.
- Affiliations
- Gachon University (KR)
- Naver (South Korea) (KR)
- Corresponding author
- Park, Eunjeong
- Choi, Sungchul
- Venue
- Scientometrics
- Published
- 2020-07-13
- Volume
- 124
- Issue
- 3
- Pages
- 1907-1922
- ISSN
- 0138-9130 (ISSN-L)
- 1588-2861
- OpenAlex
- W3043016077
- Cited by
- 186
- References
- 18
- Citations by year
- 2026: 17 · 2025: 16 · 2024: 25 · 2023: 38 · 2022: 39 · 2021: 35 · 2020: 12
- FWCI
- 29.17
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Scientometrics (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Research Foundation of Korea (NRF-2018R1D1A1B07045825, NRF-2015R1C1A1A01056185)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- citation recommendation, BERT, graph convolutional networks, context encoder, scientific writing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review