A meta-analysis of semantic classification of citations
Open accessAbstract
The aim of this literature review is to examine the current state of the art in the area of citation classification. In particular, we investigate the approaches for characterizing citations based on their semantic type. We conduct this literature review as a meta-analysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is placed on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analyzing the fine-grained linguistic features in the surrounding text of citations. The sections are organized based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, data sets, preprocessing methods, extraction of contextual and noncontextual features, and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the citation types for research evaluation, the challenges faced by the researchers in the process, and the existing research gaps in this field.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
SciCite and ACL-ARC datasets have imbalanced distributions.
“The main problem of these datasets is represented by their imbalanced distribution (see Table 1), which is a major issue when trying to solve the CIC task with automated meth- ods (Kunnath et al., 2021).”
From CiteFusion: an ensemble framework for citation intent classification harnessing dual-model binary couples and SHAP analyses (2025) · cites this paper as Kunnath et al., 2021
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218 · see citing works
Garfield, E. (1972) · Science · doi:10.1126/science.178.4060.471 · Cited by 2,788 · cited 2× · see citing works
“Citation analysis has been a subject of study for several decades, with the work of Garfield (1972) being among the most pioneering.”
What do citation counts measure? A review of studies on citing behavior
Bornmann, L., & Daniel, H. (2008) · Journal of Documentation · doi:10.1108/00220410810844150 · Cited by 1,607 · cited 2× · see citing works
“Unlike the previous survey papers in this domain (Bornmann and Daniel, 2008; HERNÁNDEZ-ALVAREZ and GOMEZ, 2016; Tahamtan and Bornmann, 2019), we focused not just on the available methods for citation classification and the citation context analysis but also the different phases of the general…”
BackgroundMethodCitations, Citation Indicators, and Research Quality: An Overview of Basic Concepts and Theories
Aksnes, D. W., Langfeldt, L., & Wouters, P. (2019) · Sage Open · doi:10.1177/2158244019829575 · Cited by 1,257 · see citing works
Birch, A., & Osborne, M. (2011) · The Association for Computational Linguistics · Cited by 1,196 · see citing works
An Overview of Microsoft Academic Service (MAS) and Applications
Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.-J. P., & Wang, K. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2740908.2742839 · Cited by 968 · see citing works
Is citation analysis a legitimate evaluation tool?
Garfield, E. (1979) · Scientometrics · doi:10.1007/bf02019306 · Cited by 911 · see citing works
Microsoft Academic Graph: When experts are not enough
Wang, K., Shen, Z., Huang, C., Wu, C.-H., Dong, Y.-X., & Kanakia, A. (2020) · Quantitative Science Studies · doi:10.1162/qss_a_00021 · Cited by 808 · see citing works
Some Results on the Function and Quality of Citations
Moravcsik, M. J., & Murugesan, P. (1975) · Social Studies of Science · doi:10.1177/030631277500500106 · Cited by 693 · see citing works
Nigel Gilbert, G. (1977) · Social Studies of Science · doi:10.1177/030631277700700112 · Cited by 691 · see citing works
Vanderwende, L., Daumé, H., & Kirchhoff, K. (2013) · North American Chapter of the Association for Computational Linguistics · Cited by 685 · see citing works
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 · see citing works
Automatic classification of citation function
Teufel, S., Siddharthan, A., & Tidhar, D. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610091 · Cited by 437 · see citing works
Content Analysis of References: Adjunct or Alternative to Citation Counting?
Chubin, D. E., & Moitra, S. D. (1975) · Social Studies of Science · doi:10.1177/030631277500500403 · Cited by 357 · see citing works
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
Georgila, K., Wolters, M. K., Vasilis Karaiskos, B., Kronenthal, M., Logie, R. H., Mayo, N., Moore, J. D., & Watson, M. (2008) · Cited by 292 · see citing works
ParsCit: an Open-source CRF Reference String Parsing Package
Councill, I. G., Giles, C. L., & Kan, M. (2008) · Cited by 289 · see citing works
Citation Analysis and Discourse Analysis Revisited
White, H. D. (2004) · Applied Linguistics · doi:10.1093/applin/25.1.89 · Cited by 260 · see citing works
Measuring academic influence: Not all citations are equal
Zhu, X., Turney, P. D., Lemire, D., & Vellino, A. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23179 · Cited by 256 · see citing works
Private acts and public objects: An investigation of citer motivations
Brooks, T. A. (1985) · Journal of the American Society for Information Science · doi:10.1002/asi.4630360402 · Cited by 256 · see citing works
An interview-based study of the functions of citations in academic writing across two disciplines
Harwood, N. (2009) · Journal of Pragmatics · doi:10.1016/j.pragma.2008.06.001 · Cited by 255 · see citing works
Lopez, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04346-8_62 · Cited by 244 · see citing works
The norms of citation behavior: Prolegomena to the footnote
Kaplan, N. (1965) · American Documentation · doi:10.1002/asi.5090160305 · Cited by 244 · see citing works
Bird, S., Dale, R., Dorr, B. J., Gibson, B., Joseph, M. T., Kan, M., Lee, D., Powley, B., Radev, D., & Tan, Y. F. (2008) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4pzr64y6ri9j · Cited by 244 · see citing works
Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence
Albrecht, S. V., Crandall, J. W., & Ramamoorthy, R. (2015) · National Conference on Artificial Intelligence · Cited by 243 · see citing works
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 242 · see citing works
Citation Analysis and Discourse Analysis
Swales, J. M. (1986) · Applied Linguistics · doi:10.1093/applin/7.1.39 · Cited by 240 · see citing works
Tahamtan, I., & Bornmann, L. (2019) · Scientometrics · doi:10.1007/s11192-019-03243-4 · Cited by 236 · see citing works
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 8× · see citing works
“Popular deep learning approaches for citation classification rely on word representations like Global Vectors for Word Representation (GloVe), Embeddings from Language Models (ELMo), and Bidirectional Encoder Representations from Transformers (BERT) for capturing the semantics from citation contexts (Beltagy, Lo, & Co…”
BackgroundMethodResultInfluentialMeasuring 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 · see citing works
Highly cited old papers and the reasons why they continue to be cited
Oppenheim, C., & Renn, S. P. (1978) · Journal of the American Society for Information Science · doi:10.1002/asi.4630290504 · Cited by 200 · see citing works
CERMINE: automatic extraction of structured metadata from scientific literature
Tkaczyk, D., Szostek, P., Fedoryszak, M., Dendek, P. J., & Bolikowski, Ł. (2015) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-015-0249-8 · Cited by 199 · see citing works
Science Studies: Bibliometric and Content Analysis
Spiegel-Rösing, I. (1977) · Social Studies of Science · doi:10.1177/030631277700700111 · Cited by 175 · see citing works
CiTO, the Citation Typing Ontology
Shotton, D. M. (2010) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-1-s1-s6 · Cited by 161 · see citing works
Sentiment Analysis of Citations using Sentence Structure-Based Features
Athar, A. (2011) · Cited by 158 · see citing works
Coherent Citation-Based Summarization of Scientific Papers
Abu-Jbara, A., & Radev, D. (2011) · Cited by 128 · see citing works
Identifying Meaningful Citations
Valenzuela, M., Ha, V., & Etzioni, O. (2015) · National Conference on Artificial Intelligence · Cited by 124 · see citing works
Concentration of the Most-Cited Papers in the Scientific Literature: Analysis of Journal Ecosystems
Ioannidis, J. P. A. (2006) · PLoS ONE · doi:10.1371/journal.pone.0000005 · Cited by 122 · see citing works
An annotation scheme for citation function
Teufel, S., Siddharthan, A., & Tidhar, D. (2006) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.3115/1654595.1654612 · Cited by 121 · see citing works
(2013) · Cited by 121 · see citing works
Nanba, H., Kando, N., & Okumura, M. (2011) · Advances in Classification Research Online · doi:10.7152/acro.v11i1.12774 · Cited by 115 · see citing works
NLP-driven citation analysis for scientometrics
Jha, R. K., JBARA, A.-A., Qazvinian, V., & Radev, D. (2016) · Natural Language Engineering · doi:10.1017/s1351324915000443 · Cited by 108 · see citing works
The use of citations in literary research: A preliminary classification of citation functions
(1979) · The Library Quarterly · doi:10.1086/600930 · Cited by 104 · cited 4× · see citing works
“Frost (1979) addressed the question of finding classification functions common to both scientific and literary research. As subjective opinion has more importance than factual evidence in literary research, Frost (1979) designed a classification scheme specifically for humanities.”
BackgroundInfluentialContext-Enhanced Citation Sentiment Detection
Athar, A., & Teufel, S. (2012) · Cited by 104 · see citing works
Towards an Automated Citation Classifier
Garzone, M., & Mercer, R. E. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45486-1_28 · Cited by 101 · see citing works
Survey about citation context analysis: Tasks, techniques, and resources
Hernández-Álvarez, M., & Gomez, J. M. (2015) · Natural Language Engineering · doi:10.1017/s1351324915000388 · Cited by 90 · see citing works
Constantin, A., Pettifer, S. R., & Воронков, А. (2013) · ACM Symposium on Document Engineering (DocEng) · doi:10.1145/2494266.2494271 · Cited by 89 · see citing works
(2011) · Cited by 84 · see citing works
Qayyum, F., & Afzal, M. (2018) · Scientometrics · doi:10.1007/s11192-018-2961-x · Cited by 77 · see citing works
Hassan, S., Safder, I., Akram, A., & Kamiran, F. (2018) · Scientometrics · doi:10.1007/s11192-018-2767-x · Cited by 70 · see citing works
Hou, W., Li, M., & Niu, D. (2011) · BioEssays · doi:10.1002/bies.201100067 · Cited by 62 · see citing works
Yousif, A., Niu, Z., Chambua, J., & Khan, Z. Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.021 · Cited by 60 · see citing works
Identifying Important Citations Using Contextual Information from Full Text
Hassan, S., Akram, A., & Haddawy, P. (2017) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2017.7991558 · Cited by 59 · see citing works
Detection of Implicit Citations for Sentiment Detection
Athar, A., & Teufel, S. (2012) · Cited by 57 · see citing works
Citation function, polarity and influence classification
Hernández-Álvarez, M., Gomez, J. M., & Martínez-Barco, P. (2017) · Natural Language Engineering · doi:10.1017/s1351324916000346 · Cited by 53 · see citing works
Neural ParsCit: a deep learning-based reference string parser
Prasad, A., Kaur, M., & Kan, M. (2018) · International Journal on Digital Libraries · doi:10.1007/s00799-018-0242-1 · Cited by 53 · see citing works
Towards a Generic and Flexible Citation Classifier Based on a Faceted Classification Scheme
Jochim, C., & Schütze, H. (2012) · Cited by 51 · see citing works
The linguistic patterns and rhetorical structure of citation context: An approach using n-grams
Bertin, M., Atanassova, I., Sugimoto, C. R., & Lariviere, V. (2016) · Scientometrics · doi:10.1007/s11192-016-2134-8 · Cited by 50 · see citing works
Citations, contexts, and humanistic discourse: Toward automatic extraction and classification
Sula, C. A., & Miller, M. (2014) · Literary and Linguistic Computing · doi:10.1093/llc/fqu019 · Cited by 46 · see citing works
A Study of Lexical Distribution in Citation Contexts through the IMRaD Standard
Bertin, M., & Atanassova, I. (2014) · Cited by 44 · see citing works
A New Approach for Scientific Citation Classification Using Cue Phrases
Pham, S. B., & Hoffmann, A. (2003) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24581-0_65 · Cited by 42 · see citing works
Automatically classifying the role of citations in biomedical articles.
Agarwal, S., Choubey, L., & Yu, H. Q. (2010) · PubMed Central · Cited by 42 · see citing works
Proceedings of the ACL 2011 Student Session
Petrović, S., Pitler, E., & Selfridge, E. O. (2011) · Cited by 41 · see citing works
A Multi-Layered Annotated Corpus of Scientific Papers
Fisas, B., Ronzano, F., & Saggion, H. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3adnqbj837bu · Cited by 41 · see citing works
Using Hedges to Classify Citations in Scientific Articles
Marco, C. D., Kroon, F. W., & Mercer, R. E. (2006) · The Kluwer international series on information retrieval · doi:10.1007/1-4020-4102-0_19 · Cited by 38 · see citing works
Lauscher, A., Ko, B., Kuehl, B., Johnson, S., Cohan, A., Jurgens, D., & Lo, K. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.137 · Cited by 38 · see citing works
Important citation identification by exploiting content and section-wise in-text citation count
Nazir, S., Asif, M., Ahmad, S., Bukhari, F., Afzal, M. T., & Aljuaid, H. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228885 · Cited by 33 · see citing works
Wang, M., Zhang, J., Jiao, S., Zhang, X., Zhu, N., & Chen, G. (2020) · Scientometrics · doi:10.1007/s11192-020-03677-1 · Cited by 32 · see citing works
An Authoritative Approach to Citation Classification
Pride, D., & Knoth, P. (2020) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3383583.3398617 · Cited by 31 · see citing works
Self-citationTowards Fine-grained Citation Function Classification
Li, X., He, Y., Meyers, A., & Grishman, R. (2013) · Cited by 31 · see citing works
Lauscher, A., Glavaš, G., Ponzetto, S. P., & Eckert, K. (2017) · doi:10.1145/3127526.3127531 · Cited by 26 · see citing works
Neural Multi-task Learning for Citation Function and Provenance
Su, X., Prasad, A., Kan, M., & Sugiyama, K. (2019) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2019.00122 · Cited by 25 · see citing works
Sharing Is Caring: The Future of Shared Tasks
Nissim, M., Abzianidze, L., Evang, K., van der Goot, R., Haagsma, H., Plank, B., & Wieling, M. B. (2017) · Computational Linguistics · doi:10.1162/coli_a_00304 · Cited by 21 · see citing works
Do citations and readership identify seminal publications?
Herrmannová, D., Patton, R. M., Knoth, P., & Stahl, C. G. (2018) · Scientometrics · doi:10.1007/s11192-018-2669-y · Cited by 21 · see citing works
Self-citationPride, D., & Knoth, P. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67008-9_48 · Cited by 20 · see citing works
Self-citationBertin, M., & Atanassova, I. (2012) · D-Lib Magazine · doi:10.1045/july2012-bertin · Cited by 19 · see citing works
Exploring Automatic Citation Classification
Radoulov, R. (2008) · UWSpace (University of Waterloo) · Cited by 18 · see citing works
Scientific Authoring Support: A Tool to Navigate in Typed Citation Graphs
Schäfer, U., & Kasterka, U. (2010) · Cited by 17 · see citing works
Annotated Corpus for Citation Context Analysis
Hernández-Álvarez, M., Soriano, J. G., & Martínez-Barco, P. (2016) · Latin-American Journal of Computing · doi:10.5281/zenodo.5748522 · Cited by 17 · see citing works
Citation Analysis with Neural Attention Models
Munkhdalai, T., Lalor, J. P., & Yu, H. Q. (2016) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/w16-6109 · Cited by 17 · see citing works
Lexical and Syntactic cues to identify Reference Scope of Citance.
Aggarwal, P., & Sharma, R. (2016) · Cited by 16 · see citing works
Incidental or influential? – A decade of using text-mining for citation function classification.
Pride, D., & Knoth, P. (2017) · Open Research Online - ORO (The Open University) · Cited by 15 · see citing works
Self-citationCitation Block Determination Using Textual Coherence
Kaplan, D., Tokunaga, T., & Teufel, S. (2016) · Journal of Information Processing · doi:10.2197/ipsjjip.24.540 · Cited by 14 · see citing works
The Importance of Fine-Grained Cue Phrases in Scientific Citations
Mercer, R. E., & Marco, C. D. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44886-1_49 · Cited by 13 · see citing works
Citance-based retrieval and summarization using IR and machine learning
Karimi, S., Moraes, L. F. T., Das, A., Shakery, A., & Verma, R. M. (2018) · Scientometrics · doi:10.1007/s11192-018-2785-8 · Cited by 13 · see citing works
Piotrowski, M., Mahlow, C., & Dale, R. (2010) · Cited by 13 · see citing works
ACT: An Annotation Platform for Citation Typing at Scale
Pride, D., Knoth, P., & Harag, J. (2019) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2019.00055 · Cited by 11 · see citing works
Self-citationDetecting Citation Types Using Finite-State Machines
Le, M.-H., Ho, T.-B., & Nakamori, Y. (2006) · Lecture notes in computer science · doi:10.1007/11731139_32 · Cited by 10 · see citing works
Semi-Automatic Annotation for Citation Function Classification
Bakhti, K., Niu, Z., & Nyamawe, A. S. (2018) · International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) · doi:10.1109/iccairo.2018.00016 · Cited by 10 · see citing works
Using Heterogeneous Features for Scientific Citation Classification
Xu, H., Martin, E., & Mahidadia, A. (2013) · doi:10.13140/2.1.2737.2484 · Cited by 10 · see citing works
A preliminary study to compare deep learning with rule-based approaches for citation classification
Perier-Camby, J., Bertin, M., Atanassova, I., & Armetta, F. (2019) · HAL · Cited by 10 · see citing works
Automatic classification of citation function by new linguistic features
Meng, R., Lu, W. P., Chi, Y., & Han, S. (2017) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · doi:10.9776/17349 · Cited by 9 · see citing works
Cabanac, G., Chandrasekaran, M. K., Frommholz, I., Jaidka, K., Kan, M., Mayr, P., & Wolfram, D. (2016) · Cited by 8 · see citing works
Important Citation Identification by Exploiting the Optimal In-text Citation Frequency
Nazir, S., Asif, M., & Ahmad, S. (2020) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet48479.2020.9048224 · Cited by 7 · see citing works
Identifying Citation Contexts: a Review of Strategies and Goals.
Rotondi, A., Iorio, A. D., & Limpens, F. (2018) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.3594 · Cited by 4 · see citing works
Citation Context Analysis using Word-Graph
Zafar, L., Ahmed, U., & Islam, M. A. (2019) · International Conference on Communication, Computing and Digital Systems (C-CODE) · doi:10.1109/c-code.2019.8680976 · Cited by 3 · see citing works
Cano (1989) · doi:10.1002/(sici)1097-4571(198907)40:4
Flagged first, then by citations
4 of the 101 references couldn’t be matched to a known paper.
Research works printed but not linked: 11
- Dong and Schäfer (2011) (1) Background-65.04% 1768 instances & 122 papers from ACL Anthology (2007 and 2008) • Use of Ensemble-style self-training reduces the manual annotation work (2) Fundamental idea-23.80% (3) Technical basis-7.18% (4) Comparison-3.95%
- Jochim and Schütze (2012) (1) Conceptual-89.2% vs. Operational-10.8% 84 papers and 2008 citation from papers in 2004 ACL Proceedings (ARC) • Annotation of four facets using Moravscik's scheme instead of a single label (2) Organic-10.1% vs. Perfunctory-89.9% (3) Evolutionary-89.8% vs. Juxtapositional-10.2% (4) Confirmative-91.4% vs. Negational-8.6%
- Abu-Jbara et al. (2013) Purpose: (1) Criticizing-14.7% 3,271 instances from 30 papers in ACL Anthology Network (AAN) • 47% of citations belong to the class Neutral (2) Comparison-8.5% • Citation Purpose classification Macro-Fscore: 58.0% (3) Use-17.7% (4) Substantiating-7% (5) Basis-5% (6) Neutral-47% Polarity: (1) Positive-30% (2) Negative-12% (3) Neutral-58%
- Xu et al. (2013) (1) Functional-48.4% ACL Anthology Network corpus (AAN) • Self-citations are skewed to the class Functional (2) Perfunctory-50% • Authors citing more has more functional citations (3) Fallback-1.6% Quantitative Science Studies 21 https://www.wsdm-conference.org/2020/wsdm-cup-2020.php 22 https://lightgbm.readthedocs.io/en/latest/ 23 https://www.biendata.xyz/competition/wsdm2020/ 24 https://wosp.core.ac.uk/jcdl2020/index.html#dataset 25 https://2020.jcdl.org/ 26 https://www.kaggle.com/c/3c-shared-task-purpose/ 27 https://www.kaggle.com/c/3c-shared-task-influence/ 28 https://www.kaggle.com/c/about/inclass REFERENCES
- de Andrade, C. M. V., & Gonçalves, M. A. (2020). Combining rep- resentations for effective citation classification. In Proceedings of the 8th International Workshop on Mining Scientific Publications (pp. 54-58). Wuhan, China: Association for Computational Lin- guistics. https://aclanthology.org/2020.wosp-1.
- Chiang Mai, Thailand: Asian Federation of Natural Language Processing. https://aclanthology.org/I11-1070.
- Garfield, E. (1965). Can citation indexing be automated? In M. E. Stevens, V. E. Giuliano, & L. B. Heilprin (Eds.), Statistical associ- ation methods for mechanized documentation, symposium proceedings, vol. 269, pp. 189-192. Washington.
- Kunnath, S. N., Pride, D., Gyawali, B., & Knoth, P. (2020). Over- view of the 2020 WOSP 3C citation context classification task. In Proceedings of the 8th International Workshop on Mining Scientific Publications (pp. 75-83). Wuhan, China: Association for Computational Linguistics. https://aclanthology.org/2020 .wosp-1.12
- Mishra, S., & Mishra, S. (2020a). Scubed at 3C task A-A simple baseline for citation context purpose classification. In Proceed- ings of the 8th International Workshop on Mining Scientific Publications (pp. 59-64). Wuhan, China. Association for Compu- tational Linguistics. https://aclanthology.org/2020.wosp-1.
- Mishra, S., & Mishra, S. (2020b). Scubed at 3C task B-A simple baseline for citation context influence classification. In Proceed- ings of the 8th International Workshop on Mining Scientific Pub- lications (pp. 65-70). Association for Computational Linguistics. https://aclanthology.org/2020.wosp-1.10
- Nakov, P. I., Schwartz, A. S., Hearst, M., et al. (2004). Citances: Citation sentences for semantic analysis of bioscience text. In Proceedings of the SIGIR, vol. 4, pp. 81-88. Citeseer.
Cited by
Sorted by citations
Wang, Z., Zhang, H., Chen, J., & Chen, H. (2024) · Journal of Informetrics · doi:10.1016/j.joi.2024.101587 · Cited by 31 · see citing works
CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers
Lahiri, A., Sanyal, D. K., & Mukherjee, I. (2023) · 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL) · doi:10.1109/jcdl57899.2023.00017 · Cited by 24 · see citing works
Jiang, X., & Liu, J. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24748 · Cited by 20 · see citing works
Prompting Strategies for Citation Classification
Kunnath, S. N., Pride, D., & Knoth, P. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615018 · Cited by 19 · see citing works
Self-citationHighly cited educational technology journal articles: a descriptive and critical analysis
Mertala, P., Moens, E., & Teräs, M. (2022) · Learning Media and Technology · doi:10.1080/17439884.2022.2141253 · Cited by 22 · see citing works
Leveraging LLMs for Efficient Topic Reviews
Gana, B., Leiva-Araos, A., Allende‐Cid, H., & García, J. (2024) · Applied Sciences · doi:10.3390/app14177675 · Cited by 15 · see citing works
OpCitance: Citation contexts identified from the PubMed Central open access articles
Hsiao, T., & Torvik, V. I. (2023) · Scientific Data · doi:10.1038/s41597-023-02134-x · Cited by 15 · see citing works
Citation recommendation based on argumentative zoning of user queries
Ma, S.-T., Zhang, C.-Z., Zhang, H., & Gao, Z. (2025) · J. Informetrics · doi:10.1016/j.joi.2024.101607 · Cited by 15 · see citing works
Yang, A. J., & Deng, S. (2024) · Quantitative Science Studies · doi:10.1162/qss_a_00323 · Cited by 11 · see citing works
Does the Market of Citations Reward Reproducible Work?
Raff, E. (2023) · ACM Conference on Reproducibility and Replicability (ACM REP) · doi:10.1145/3589806.3600041 · Cited by 12 · see citing works
Contextualised segment-wise citation function classification
Jiang, X., & Chen, J. (2023) · Scientometrics · doi:10.1007/s11192-023-04778-3 · Cited by 10 · see citing works
Song, D., Wang, W., Fan, Y., Xing, Y., & Zeng, A. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102996 · Cited by 7 · see citing works
Paolini, L., Vahdati, S., Di Iorio, A., Wardenga, R., Heibi, I., & Peroni, S. (2025) · Scientometrics · doi:10.1007/s11192-025-05418-8 · Cited by 8 · see citing works
“Early rule-based systems (Garzone & Mercer, 2000; Nanba et al., 2011) relied on handcrafted heuristics but exhibited limited generalizability due to their dependence on predefined patterns (Kunnath et al., 2021), resulting in rigid frameworks.”
da Silva, J. A. T., & Nazarovets, S. (2023) · Scientometrics · doi:10.1007/s11192-023-04769-4 · Cited by 5 · see citing works
Ensembling approaches to citation function classification and important citation screening
Jiang, X. (2025) · Scientometrics · doi:10.1007/s11192-025-05265-7 · Cited by 5 · see citing works
Obadage, R. R., Rajtmajer, S. M., & Wu, J. F. (2024) · ACM Conference on Reproducibility and Replicability (ACM REP) · doi:10.1145/3641525.3663628 · Cited by 5 · see citing works
The attention inequality of scientists: A core-periphery structure perspective
Wang, H., Huang, W.-B., & Bu, Y. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104170 · Cited by 5 · see citing works
Contextualised Modelling for Effective Citation Function Classification
Jiang, X., Cai, C., Fan, W., Liu, T., & Chen, J. (2022) · International Conference on Natural Language Processing and Information Retrieval (NLPIR) · doi:10.1145/3582768.3582769 · Cited by 4 · see citing works
Phan, T.-A., Bui, K. N., & Jung, J. K. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104743 · Cited by 3 · see citing works
Inline Citation Classification Using Peripheral Context and Time-Evolving Augmentation
Gupta, P., Atri, Y. K., Nagvenkar, A. S., Dasgupta, S., & Chakraborty, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33383-5_1 · Cited by 3 · see citing works
Linguistic perspectives in deciphering citation function classification
Bertin, M., & Atanassova, I. (2024) · Scientometrics · doi:10.1007/s11192-024-05082-4 · Cited by 3 · see citing works
Bag of Tricks for Citation Intent Classification via SciBERT
Motrichenko, D., Nedumov, Y., & Skorniakov, K. (2021) · Ivannikov ISPRAS Open Conference (ISPRAS) · doi:10.1109/ispras53967.2021.00022 · Cited by 3 · see citing works
Deepening citation understanding in scientific literature via LLM-powered context extraction
Nguyen, T. H., Pruski, C., & Silveira, M. D. (2026) · Scientometrics · doi:10.1007/s11192-026-05637-7 · Cited by 2 · see citing works
Science Evaluation: Peer Review, Bibliometrics, and Research Impact Assessment
Redman, B. K. (2023) · Springer eBooks · doi:10.1007/978-3-031-27111-3_8 · Cited by 2 · see citing works
MADC-ED: A Multimodal Approach for Dimensional Classification in Engineering Drawings
Xu, H., He, Z., Xu, N., & Lv, W. (2025) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict64582.2025.00061 · Cited by 2 · see citing works
Li, X., Du, H., Bu, Y., Ai, M., Huang, J., & Jia, T. (2025) · Journal of Informetrics · doi:10.1016/j.joi.2025.101730 · Cited by 1 · see citing works
Citation and Referencing Practices in Argumentative Essay Writing by Upper-Intermediate EFL Students
Капранов, О. (2023) · Studia Anglica Resoviensia · doi:10.15584/sar.2023.20.4 · Cited by 1 · see citing works
Micro Citation Importance Identification and Its Application to Literature Evaluation
Nie, W., & Ou, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-57860-1_24 · Cited by 1 · see citing works
Katsadaki, E., & Kokla, M. (2024) · International Journal of Environment and Climate Change · doi:10.9734/ijecc/2024/v14i84361 · Cited by 1 · see citing works
Citation Context Extraction Using a Transformer-Based Model with a Question-Answering Task
Khanyi, M., van Lill, M., & Dunaiski, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-96262-2_19 · Cited by 1 · see citing works
Anatomization of Neural Networks based models for Semantic Analysis of Tabular dataset
Bharath, B. S., Bollineni, J., Mandala, S. P., Ganguly, T., Amudha, J., Shukla, N., & Joseph, R. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10544176
Abdelhamed, R., & Essgaer, M. (2024) · IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA) · doi:10.1109/mi-sta61267.2024.10599683
Citation Intent Classification Using Transformers
C, R. G. H., Raj, K. S., & Kumar, M. A. (2024) · Students Conference on Engineering and Systems · doi:10.1109/sces61914.2024.10652428
Levkivskyi, D., Vysotska, V., Chyrun, L., Ushenko, Y., Uhryn, D. I., & Hu, C. (2025) · International Journal of Information Engineering and Electronic Business · doi:10.5815/ijieeb.2025.02.01
Wissenschaftliche Bewertung: Peer-Review, Bibliometrie und Forschungsimpact-Bewertung
Redman, B. K. (2025) · Springer eBooks · doi:10.1007/978-3-031-92469-9_8
Methods and models for the analysis of scientific activity
Gubanov, D., Zhilyakova, L. Y., Kuznetsov, O. P., & Chkhartishvili, A. G. (2025) · Ontology of Designing · doi:10.18287/2223-9537-2025-15-4-578-597
Multi-intent prediction of scientific literature based on heterogeneous graph fusion network
Quan, Z., Mao, J., & Li, G. (2025) · Scientometrics · doi:10.1007/s11192-025-05493-x
SECite: Analyzing and Summarizing Citations in Software Engineering Literature
Pyreddy, S. R., Pathan, K. V., Masum, H., & Zaman, T. S. (2026) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc67433.2026.11393720
Nasilnikov, E. V., & Maksimenko, O. I. (2026) · Professional Discourse & Communication · doi:10.24833/2687-0126-2026-8-1-27-52
Predicting the impact of scientific articles through semantic analysis of abstracts
Süzen, N., Gorban, A. N., Levesley, J., & Mirkes, E. M. (2026) · Scientometrics · doi:10.1007/s11192-026-05647-5 · Cited by 1 · see citing works
Liu, Y., Yang, J., Liu, G., & Huang, L. (2026) · Scientometrics · doi:10.1007/s11192-026-05658-2
Darold, D. L. (2026) · Research Square · doi:10.21203/rs.3.rs-9589637/v1
Zhang, L., Wan, Z., Cheng, M., Shi, S., & Gou, Z. (2026) · Scientometrics · doi:10.1007/s11192-026-05813-9
- Authors
- Kunnath, S. N., Herrmannova, D., Pride, D., & Knoth, P.
- Affiliations
- The Open University (GB)
- Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
- Venue
- Quantitative Science Studies
- Publisher
- MIT Press
- Published
- 2021
- Volume
- 2
- Issue
- 4
- Pages
- 1170-1215
- ISSN
- 2641-3337 (ISSN-L)
- OpenAlex
- W3207782692
- Cited by
- 43
- References
- 101
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 11 · 2023: 10 · 2022: 3 · 2021: 1
- FWCI
- 0.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Quantitative Science Studies (publishedVersion, cc-by)
- Open Research Online (The Open University) (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Horizon 2020 Framework Programme (824612)
- Joint Information Systems Committee
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- citation classification, contextual features, research evaluation, bibliometric analysis
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
