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Semantically Orthogonal Framework for Citation Classification: Disentangling Intent and Content
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“Although Duan and Tan (2026) noted this conflation between citation intent and cited content type, their work remains centered on the citing context and does not analyze the cited paper's knowledge contributions directly.”
From From citation intent to knowledge contribution: Classifying what cited papers actually contribute (2026) · cites this paper as Duan and Tan (2026)
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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
Efficient Memory Management for Large Language Model Serving with PagedAttention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J., Zhang, H., & Stoica, I. (2023) · Proceedings of the 29th Symposium on Operating Systems Principles · doi:10.1145/3600006.3613165 · Cited by 1,817
Efficient Memory Management for Large Language Model Serving with PagedAttention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., & Stoica, I. (2023) · arXiv · doi:10.48550/arxiv.2309.06180 · Cited by 1,802
ChatGPT outperforms crowd workers for text-annotation tasks
Gilardi, F., Alizadeh, M., & Kubli, M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305016120 · Cited by 1,197
Gemma Team, Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ramé, A., Rivière, M., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.19786 · Cited by 1,156
S2ORC: The Semantic Scholar Open Research Corpus
Lo, K., Wang, L. L., Neumann, M., Kinney, R., & Weld, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.447 · Cited by 457
Reducing Workload in Systematic Review Preparation Using Automated Citation Classification
Cohen, A. M., Hersh, W. R., Peterson, K., & Yen, P.-Y. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1929 · Cited by 411 · cited 1×
“…understanding provides the technological backbone for a new generation of digital libraries and intelligent research assistants, enabling advanced services such as automated literature reviews [3,15,27,28], the detection of scientific controversies [41], and impact-aware scientific search [35,63].”
Structural Scaffolds for Citation Intent Classification in Scientific Publications
Cohan, A., Ammar, W., van Zuylen, M., & Cady, F. (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/n19-1361 · Cited by 214 · cited 3×
“Despite the progress enabled by widely used frameworks such as CiTO [52], ACL-ARC [28], SciCite [14], and ACT/ACT2 [40, 45], several structural limitations persist.”
Measuring the Evolution of a Scientific Field through Citation Frames
Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00028 · Cited by 212
Nicholson, J. M., Mordaunt, M., Lopez, P., Uppala, A., Rosati, D., Rodrigues, N. P., Grabitz, P., & Rife, S. C. (2021) · bioRxiv (published in Quantitative Science Studies) · doi:10.1101/2021.03.15.435418 · Cited by 202
Flagged first, then by citations
Research works printed but not linked: 51
- Abu-Jbara, A., Ezra, J., Radev, D.: Purpose and polarity of citation: towards NLP-based bibliometrics. In: Vanderwende, L., Daumé III, H., Kirchhoff, K. (eds.) Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 596–606. Association for Computational Linguistics, Atlanta, Georgia (2013). https://aclanthology.org/N13-1067/
- Allen Institute for AI: Semantic Scholar (2025). https://www.semanticscholar.org/
- Anderson, M.H., Lemken, R.K.: Citation context analysis as a method for conducting rigorous and impactful literature reviews. Organ. Res. Methods 26(1), 77–106 (2020). https://doi.org/10.1177/1094428120969905 · DOI
- Araabi, A., Niculae, V., Monz, C.: Joint dropout: improving generalizability in low-resource neural machine translation through phrase pair variables. In: Utiyama, M., Wang, R. (eds.) Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pp. 12–25. Asia-Pacific Association for Machine Translation, Macau SAR, China (2023). https://aclanthology.org/2023.mtsummit-research.2/
- Artstein, R., Poesio, M.: Survey article: inter-coder agreement for computational linguistics. Comput. Linguist. 34(4), 555–596 (2008). https://doi.org/10.1162/coli.07-034-R2. https://aclanthology.org/J08-4004/ · DOI · Paper page
- Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet project. In: 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1, pp. 86–90. Association for Computational Linguistics, Montreal, Quebec, Canada (1998). https://doi.org/10.3115/980845.980860. https://aclanthology.org/P98-1013/ · DOI
- Bascur, J.P., Verberne, S., van Eck, N.J., Waltman, L.: Academic information retrieval using citation clusters: in-depth evaluation based on systematic reviews. Scientometrics 128(5), 2895–2921 (2023). https://doi.org/10.1007/s11192-023-04681-x · DOI
- Bassignana, E., Plank, B.: CrossRE: a cross-domain dataset for relation extraction. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 3592–3604. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (2022). https://doi.org/10.18653/v1/2022.findings-emnlp.263. https://aclanthology.org/2022.findings-emnlp.263/ · DOI
- Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3615–3620. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1371. https://aclanthology.org/D19-1371/ · DOI · Paper page
- Berrebbi, D., Huynh, N., Balalau, O.: Graphcite: citation intent classification in scientific publications via graph embeddings, pp. 779–783. WWW 2022: The ACM Web Conference 2022. ACM (2022). https://doi.org/10.1145/3487553.3524657 · DOI
- Chen, Y., et al.: CDEvalSumm: an empirical study of cross-dataset evaluation for neural summarization systems. In: Cohn, T., He, Y., Liu, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 3679–3691. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.329. https://aclanthology.org/2020.findings-emnlp.329/ · DOI
- Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960). https://doi.org/10.1177/001316446002000104 · DOI · Paper page
- Dardas, L.A., Sallam, M., Woodward, A., Sweis, N., Sweis, N., Sawair, F.A.: Evaluating research impact based on semantic scholar highly influential citations, total citations, and altmetric attention scores: the quest for refined measures remains illusive. Publications 11(1), 5 (2023). https://doi.org/10.3390/publications11010005 · DOI
- Ding, N., et al.: OpenPrompt: an open-source framework for prompt-learning. In: Basile, V., Kozareva, Z., Stajner, S. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 105–113. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-demo.10. https://aclanthology.org/2022.acl-demo.10/ · DOI
- Fillmore, C.J., et al.: Frame semantics. Cogn. Linguist. Basic Read. 34, 373–400 (2006) · DOI
- Ghosal, T., Varanasi, K.K., Kordoni, V.: A deep multi-tasking approach leveraging on cited-citing paper relationship for citation intent classification. Scientometrics 129(2), 767–783 (2023). https://doi.org/10.1007/s11192-023-04811-5 · DOI
- Guo, M., Yang, Y., Cer, D., Shen, Q., Constant, N.: MultiReQA: a cross-domain evaluation for retrieval question answering models. In: Ben-David, E., et al. (eds.) Proceedings of the Second Workshop on Domain Adaptation for NLP, pp. 94–104. Association for Computational Linguistics, Kyiv, Ukraine (2021). https://aclanthology.org/2021.adaptnlp-1.10/
- Hettiachchi, D., et al.: Investigating and mitigating biases in crowdsourced data, pp. 331–334. CSCW 2021: Computer Supported Cooperative Work and Social Computing. ACM (2021). https://doi.org/10.1145/3462204.3481729 · DOI
- Ivanova, R.V., Kirrane, S., van Erp, M.: Comparing annotated datasets for named entity recognition in English literature. In: Calzolari, N., et al. (eds.) Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 3788–3797. European Language Resources Association, Marseille, France (2022). https://aclanthology.org/2022.lrec-1.404/ · DOI
- Jha, R., Jbara, A.A., Qazvinian, V., Radev, D.R.: NLP-driven citation analysis for scientometrics. Nat. Lang. Eng. 23(1), 93–130 (2016). https://doi.org/10.1017/s1351324915000443 · DOI
- Koloveas, P., Chatzopoulos, S., Vergoulis, T., Tryfonopoulos, C.: Can LLMs predict citation intent? An experimental analysis of in-context learning and fine-tuning on open LLMs (2025). https://arxiv.org/abs/2502.14561 · DOI
- Kunnath, S.N., Pride, D., Knoth, P.: Prompting strategies for citation classification. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, pp. 1127–1137. Association for Computing Machinery, New York (2023). https://doi.org/10.1145/3583780.3615018 · DOI
- Li, K., et al.: Probing out-of-distribution generalization in machine learning for materials (2024). https://arxiv.org/abs/2406.06489 · arXiv
- Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48 · DOI · Paper page
- Mistral AI Team: Mistral Small 3 (2025). https://mistral.ai/news/mistral-small-3/ · DOI
- Nambanoor Kunnath, S., Pride, D., Knoth, P.: Dynamic context extraction for citation classification. In: He, Y., Ji, H., Li, S., Liu, Y., Chang, C.H. (eds.) Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 539–549. Association for Computational Linguistics, Online only (2022). https://doi.org/10.18653/v1/2022.aacl-main.41. https://aclanthology.org/2022.aacl-main.41/ · DOI
- Nambanoor Kunnath, S., Stauber, V., Wu, R., Pride, D., Botev, V., Knoth, P.: ACT2: a multi-disciplinary semi-structured dataset for importance and purpose classification of citations. In: Calzolari, N., et al. (eds.) Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 3398–3406. European Language Resources Association, Marseille, France (2022). https://aclanthology.org/2022.lrec-1.363/ · DOI
- Northcutt, C., Jiang, L., Chuang, I.: Confident learning: estimating uncertainty in dataset labels. J. Artif. Intell. Res. 70, 1373–1411 (2021). https://doi.org/10.1613/jair.1.12125 · DOI · Paper page
- Northcutt, C.G., Athalye, A., Mueller, J.: Pervasive label errors in test sets destabilize machine learning benchmarks. In: Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1) (2021). https://openreview.net/forum?id=XccDXrDNLek
- Palmer, M., Gildea, D., Kingsbury, P.: The proposition bank: an annotated corpus of semantic roles. Comput. Linguist. 31(1), 71–106 (2005). https://doi.org/10.1162/0891201053630264. https://aclanthology.org/J05-1004/ · DOI
- Pride, D., Knoth, P.: An authoritative approach to citation classification. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, JCDL 2020, pp. 337–340. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3383583.3398617 · DOI
- Pride, D., Knoth, P.: An authoritative approach to citation classification. JCDL 2020: The ACM/IEEE Joint Conference on Digital Libraries in 2020. ACM (2020). https://doi.org/10.1145/3383583.3398617 · DOI
- Qazvinian, V., Radev, D.R.: Scientific paper summarization using citation summary networks. In: Scott, D., Uszkoreit, H. (eds.) Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pp. 689–696. Coling 2008 Organizing Committee, Manchester, UK (2008). https://aclanthology.org/C08-1087/ · DOI
- Qwen: Qwen2.5 technical report (2025). https://arxiv.org/abs/2412.15115 · arXiv
- Riabi, A., Mouilleron, V., Mahamdi, M., Antoun, W., Seddah, D.: Beyond dataset creation: critical view of annotation variation and bias probing of a dataset for online radical content detection. In: Rambow, O., Wanner, L., Apidianaki, M., Al-Khalifa, H., Eugenio, B.D., Schockaert, S. (eds.) Proceedings of the 31st International Conference on Computational Linguistics, pp. 8640–8663. Association for Computational Linguistics, Abu Dhabi, UAE (2025). https://aclanthology.org/2025.coling-main.578/
- Roman, M., Shahid, A., Khan, S., Koubaa, A., Yu, L.: Citation intent classification using word embedding. IEEE Access 9, 9982–9995 (2021). https://doi.org/10.1109/access.2021.3050547 · DOI
- Rücker, S., Akbik, A.: CleanCoNLL: a nearly noise-free named entity recognition dataset. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 8628–8645. Association for Computational Linguistics, Singapore (2023). https://doi.org/10.18653/v1/2023.emnlp-main.533. https://aclanthology.org/2023.emnlp-main.533/ · DOI
- Shotton, D.: CiTO, the citation typing ontology. J. Biomed. Semant. 1(Suppl. 1), S6 (2010). https://doi.org/10.1186/2041-1480-1-s1-s6 · DOI
- Shui, Z., et al.: Fine-tuning language models on multiple datasets for citation intention classification. In: Al-Onaizan, Y., Bansal, M., Chen, Y.N. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 16718–16732. Association for Computational Linguistics, Miami, Florida, USA (2024). https://doi.org/10.18653/v1/2024.findings-emnlp.974. https://aclanthology.org/2024.findings-emnlp.974/ · DOI
- Singh, S., Yadav, A., Jain, J., Shi, H., Johnson, J., Desai, K.: Benchmarking object detectors with coco: a new path forward. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) ECCV 2024, pp. 279–295. Springer, Cham (2025). https://doi.org/10.1007/978-3-031-72784-9_16 · DOI
- Spiegel-Rosing, I.: Science studies: bibliometric and content analysis. Soc. Stud. Sci. 7(1), 97–113 (2 1977). https://doi.org/10.1177/030631277700700111 · DOI · Paper page
- Team, L.: The llama 3 herd of models (2024). https://arxiv.org/abs/2407.21783 · arXiv
- Teufel, S.: Argumentative zoning: information extraction from scientific text (1999). https://www.cl.cam.ac.uk/~sht25/thesis/t1.pdf
- Teufel, S., Siddharthan, A., Tidhar, D.: Automatic classification of citation function. In: Jurafsky, D., Gaussier, E. (eds.) Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 103–110. Association for Computational Linguistics, Sydney, Australia (2006). https://aclanthology.org/W06-1613/ · DOI · Paper page
- Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp. 142–147 (2003). https://aclanthology.org/W03-0419/ · DOI · Paper page
- Tkachenko, U., Thyagarajan, A., Mueller, J.: Objectlab: automated diagnosis of mislabeled images in object detection data (2023). https://doi.org/10.48550/ARXIV.2309.00832. https://arxiv.org/abs/2309.00832 · DOI
- Valenzuela, M., Ha, V., Etzioni, O.: Identifying meaningful citations. In: AAAI Workshop: Scholarly Big Data, vol. 15, p. 13 (2015)
- Yu, H., Liu, J., Zhang, X., Wu, J., Cui, P.: A survey on evaluation of out-of-distribution generalization (2024). https://arxiv.org/abs/2403.01874 · arXiv
- Zha, D., et al.: Data-centric artificial intelligence: a survey. ACM Comput. Surv. 57(5) (2025). https://doi.org/10.1145/3711118 · DOI
- Zheng, Y., Zhang, R., Zhang, J., Ye, Y., Luo, Z.: LlamaFactory: unified efficient fine-tuning of 100+ language models. In: Cao, Y., Feng, Y., Xiong, D. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pp. 400–410. Association for Computational Linguistics, Bangkok, Thailand (2024). https://doi.org/10.18653/v1/2024.acl-demos.38. https://aclanthology.org/2024.acl-demos.38/ · DOI
- Zimmermann, E., Szeto, J., Pasquero, J., Ratle, F.: Benchmarking a benchmark: how reliable is MS-coco? (2023). https://arxiv.org/abs/2311.02709 · arXiv
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Sorted by citations
From citation intent to knowledge contribution: Classifying what cited papers actually contribute
Quan, Z., Liang, Z., Ma, M., Wei, J., Li, G., & Mao, J. (2026) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.70127
Metadata
- Authors
- Duan, C., & Tan, Z.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2025-09-15
- Pages
- 183-206
- ISSN
- 0302-9743
- 1611-3349
- Cited by
- 3
- References
- 14
- Language
- en
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 ›
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