Measuring the Evolution of a Scientific Field through Citation Frames
Open accessAbstract
Citations have long been used to characterize the state of a scientific field and to identify influential works. However, writers use citations for different purposes, and this varied purpose influences uptake by future scholars. Unfortunately, our understanding of how scholars use and frame citations has been limited to small-scale manual citation analysis of individual papers. We perform the largest behavioral study of citations to date, analyzing how scientific works frame their contributions through different types of citations and how this framing affects the field as a whole. We introduce a new dataset of nearly 2,000 citations annotated for their function, and use it to develop a state-of-the-art classifier and label the papers of an entire field: Natural Language Processing. We then show how differences in framing affect scientific uptake and reveal the evolution of the publication venues and the field as a whole. We demonstrate that authors are sensitive to discourse structure and publication venue when citing, and that how a paper frames its work through citations is predictive of the citation count it will receive. Finally, we use changes in citation framing to show that the field of NLP is undergoing a significant increase in consensus.
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ACL-ARC contains 1,941 citation instances from 186 papers.
“ACL-ARC is a dataset of citation intents released by Jurgens et al. (2018). The dataset is based on a sample of papers from the ACL Anthology Reference Corpus (Bird et al., 2008) and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field.”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
The Class Description section reports 1,969 annotated citation instances, not 1,941. It says these came from 52 fully labeled papers and 133 additional papers, totaling 185 rather than 186. Page number is unavailable in our copy. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredPrevious state-of-the-art (Jurgens et al., 2018) achieves macro F1 54.6 on ACL-ARC.
“previous state-of-the-art results of Jurgens et al. (2018) 54.6”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
Table 7 reports a macro F1 of 0.530 for the authors’ classifier, not 0.546. The Experimental Setup describes evaluation on a combined citation-function dataset, so the paper also does not establish 54.6 as an ACL-ARC result. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe proposed model achieves a new state-of-the-art on the ACL-ARC dataset with a 13.3% absolute increase in F1 score over the previous state-of-the-art.
“Our model achieves a new state-ofthe-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods.”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
Section 3.3 (page unavailable) says the paper’s methods outperformed the closest state of the art, supporting the general performance claim. It does not report a 13.3-percentage-point absolute F1 increase in the passages found; Section 3.2 describes evaluation on a combined citation-function dataset rather than establishing that figure for ACL-ARC. (LLM, conf=0.85)
Experimental — accuracy not yet measuredCitation context includes text spans in a citing paper describing a referenced work and is the primary signal in intent classification.
“A citation context includes text spans in a citing paper describing a referenced work and has been shown to be the primary signal in intent classification (Teufel et al., 2006; Abu-Jbara et al., 2013; Jurgens et al., 2018).”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
Section 3.1 treats text around a citation—including preceding and following sentences—as evidence of its purpose. Section 3.3 finds that local textual features matter, but also identifies structural and topic features among the highest-weighted features. The paper supports citation context as an important signal, not the unqualified claim that it is the primary signal. (LLM, conf=0.84)
Experimental — accuracy not yet measuredJurgens et al. (2018) expanded all pre-existing feature-based efforts on citation intent classification by proposing a comprehensive set of engineered features, including bootstrapped patterns, topic modeling, dependency-based, and metadata features.
“Jurgens et al. (2018) expanded all pre-existing featurebased efforts on citation intent classification by proposing a comprehensive set of engineered features, including boostrapped patterns, topic modeling, dependency-based, and metadata features for the task.”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
Section 3 and §3.1 describe a citation-classification system with novel bootstrapped patterns and topic-model features; Table 3 lists venue and other field metadata, and the Prototypical Argument Features section describes dependency-path features. The paper says it draws features from several earlier systems, but does not establish that it expanded *all* pre-existing feature-based efforts. (LLM, conf=0.86)
Experimental — accuracy not yet measured
For the Background and Future classes, no grammatical features were provided by Jurgens and colleagues (2018), rendering a direct comparison with our results unfeasible.
“However, for the Background and Future classes, no grammatical features were provided by Jurgens and colleagues (2018), rendering a direct comparison with our results unfeasible.”
From CiteFusion: an ensemble framework for citation intent classification harnessing dual-model binary couples and SHAP analyses (2025) · cites this paper as Jurgens et al., 2018
The Prototypical Argument Features section (page unavailable) gives grammatical-feature examples for several classes but none for BACKGROUND or FUTURE, supporting the narrower point that their class-specific examples were not provided. It also says the authors construct a prototype for each citation function, so the broader assertion that no grammatical features were provided for those classes overstates the paper. (LLM, conf=0.82)
Experimental — accuracy not yet measuredExisting models for citation intent classification are feature-based, modeling the citation context with predefined hand-engineered features and ignoring other signals that could improve prediction.
“Existing models for this problem are feature-based, modeling the citation context with respect to a set of predefined handengineered features (such as linguistic patterns or cue phrases) and ignoring other signals that could improve prediction.”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
ACL-ARC is annotated by domain experts in the NLP field.
“ACL-ARC is a dataset of citation intents released by Jurgens et al. (2018). The dataset is based on a sample of papers from the ACL Anthology Reference Corpus (Bird et al., 2008) and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field.”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
Our implementation of Jurgens et al. (2018) achieves a macro-averaged F1 score of 0.526 using 10-fold cross-validation.
“Our implementation of this model achieves a macro-averaged F1 score of 0.526 using 10-fold cross-validation”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
The results were obtained without using hand-curated features or additional linguistic resources as used in Jurgens et al. (2018).
“We note that these results were obtained without using hand-curated features or additional linguistic resources as used in Jurgens et al. (2018).”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
Adding features used in Jurgens et al. (2018) to the best model did not improve performance and caused at least 1.7% decline.
“We also experimented with adding features used in Jurgens et al. (2018) to our best model and not only we did not see any improvements, but we observed at least 1.7% decline in performance.”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
On the SciCite dataset, Jurgens et al. (2018) is the second best baseline.
“followed by Jurgens et al. (2018)”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
Jurgens et al. (2018) incorporate a variety of features, ranging from pattern-based features to topic-modeling features, to citation graph features, and also section titles and relative section position.
“Jurgens et al. (2018) incorporate a variety of features, ranging from pattern-based features to topic-modeling features, to citation graph features. They also incorporate section titles and relative section position in the paper as features.”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
Jurgens et al. (2018) proposed a six category system for citation intents.
“Jurgens et al. (2018) proposed a six category system for citation intents.”
From Structural Scaffolds for Citation Intent Classification in Scientific Publications (2019) · cites this paper as Jurgens et al., 2018
ACL-ARC provides nearly 2000 manually annotated citation contexts from the NLP domain.
“An early contribution to CIC is the ACL-ARC citations dataset (Jurgens et al., 2018), which provides nearly 2000 manually annotated citation contexts from the NLP domain.”
From CiteFusion: an ensemble framework for citation intent classification harnessing dual-model binary couples and SHAP analyses (2025) · cites this paper as Jurgens et al., 2018
Citation type or class, such as background citations and method citations, can refine the citation metric.
“potentially refined by citation type or class, such as background citations and method citations (Teufel et al., 2006; AbuRa'ed et al., 2017; Jurgens et al., 2018; Pride and Knoth, 2020; Lauscher et al., 2022).”
From What Can Natural Language Processing Do for Peer Review? (2024) · cites this paper as Jurgens et al., 2018
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · see citing works
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works
Frame Analysis: An Essay on the Organization of Experience.
Gamson, W. A., & Goffman, E. (1975) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2064022 · Cited by 11,891 · see citing works
A Caution Regarding Rules of Thumb for Variance Inflation Factors
O’brien, R. M. (2007) · Quality & Quantity · doi:10.1007/s11135-006-9018-6 · Cited by 11,217 · see citing works
(1987) · Cited by 8,955 · see citing works
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405 · see citing works
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. J., & McClosky, D. (2014) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.3115/v1/p14-5010 · Cited by 7,171 · see citing works
Genre Analysis: English in Academic and Research Settings
James, M. O., & Swales, J. M. (1992) · Modern Language Journal · doi:10.2307/329786 · Cited by 4,710 · see citing works
Applied Linear Regression Models
(1997) · Technometrics · doi:10.1080/00401706.1997.10485142 · Cited by 4,638 · see citing works
Head-Driven Statistical Models for Natural Language Parsing
Collins, M. (2003) · Computational Linguistics · doi:10.1162/089120103322753356 · Cited by 1,848 · see citing works
Graph-based approach for airborne light detection and ranging segmentation
Vilariño, D. L., Cabaleiro, J. C., Sánchez, J. M., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,760 · see citing works
Citation Indexing-Its Theory and Application in Science, Technology, and Humanities
Goodwin, J., & Garfield, E. (1980) · Technology and Culture · doi:10.2307/3104125 · Cited by 1,648 · see citing works
Computational linguistics in 1990
Karlgren, H. (1990) · International Conference on Computational Linguistics · doi:10.3115/992507.992531 · Cited by 1,247 · see citing works
The Structure of a Social Science Collaboration Network: Disciplinary Cohesion from 1963 to 1999
Moody, J. W. (2004) · American Sociological Review · doi:10.1177/000312240406900204 · Cited by 991 · see citing works
brat: a Web-based Tool for NLP-Assisted Text Annotation
Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S., & Tsujii, J. (2012) · Research Explorer (The University of Manchester) · Cited by 961 · see citing works
Quantifying Long-Term Scientific Impact
Wang, D., Song, C., & Barabási, A.-Ĺ. (2013) · Science · doi:10.1126/science.1237825 · Cited by 850 · see citing works
Three generative, lexicalised models for statistical parsing
Collins, M. (1997) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/976909.979620 · Cited by 751 · 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
Learning dictionaries for information extraction by multi-level bootstrapping
Riloff, E., & Jones, R. (1999) · Cited by 675 · see citing works
The medical research paper: Structure and functions
Nwogu, K. N. (1997) · English for Specific Purposes · doi:10.1016/s0889-4906(97)85388-4 · Cited by 602 · see citing works
Studying the history of ideas using topic models
Hall, D., Jurafsky, D. S., & Manning, C. D. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613763 · Cited by 459 · see citing works
Self-citationAutomatic 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
Pereira, F. C. N., & Warren, D. H. D. (1983) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/981311.981338 · Cited by 296 · 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
Musgrave, S. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781107445307.026 · Cited by 266 · 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
Fowler, J. H., & Aksnes, D. W. (2007) · Scientometrics · doi:10.1007/s11192-007-1777-2 · Cited by 259 · 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
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
Content‐based citation analysis: The next generation of citation analysis
Ding, Y., Zhang, G., Chambers, T., Song, M., Wang, X., & Zhai, C. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23256 · Cited by 252 · 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
Journal of the American Society for Information Science
(1984) · Journal of the American Society for Information Science · doi:10.1002/asi.4630350113 · Cited by 237 · see citing works
Why the social sciences won't become high-consensus, rapid-discovery science
Collins, R. (1994) · Sociological Forum · doi:10.1007/bf01476360 · Cited by 225 · see citing works
Evidence of complex citer motivations
Brooks, T. A. (1986) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(198601)37:1 · Cited by 225 · see citing works
Argumentative zoning information extraction from scientific text
Teufel, S. (1999) · Cited by 222 · see citing works
Gazni, A., & Didegah, F. (2011) · Scientometrics · doi:10.1007/s11192-011-0343-8 · Cited by 221 · 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
100 Million Words of English:The British National Corpus (BNC)
Leech, G. (1992) · Seoul National University Open Repository (Seoul National University) · Cited by 198 · see citing works
Yan, R., Tang, J., Liu, X., Shan, D., & Li, X. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2063757 · Cited by 179 · 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
Natural Language Processing: A Historical Review
Jones, K. S. (1994) · Springer eBooks · doi:10.1007/978-0-585-35958-8_1 · Cited by 150 · see citing works
A uniform architecture for parsing and generation
Shieber, S. M. (1988) · International Conference on Computational Linguistics · doi:10.3115/991719.991764 · Cited by 144 · see citing works
The distribution of references across texts: Some implications for citation analysis
Ding, Y., Liu, X., Guo, C., & Cronin, B. (2013) · Journal of Informetrics · doi:10.1016/j.joi.2013.03.003 · Cited by 137 · see citing works
Evidence of complex citer motivations
Brooks, T. A. (1986) · Journal of the American Society for Information Science · doi:10.1002/asi.4630370106 · Cited by 135 · see citing works
A Simple, Similarity-based Model for Selectional Preferences
Erk, K. (2007) · Cited by 134 · see citing works
Towards Multi-paper Summarization Using Reference Information
Nanba, H., & Okumura, M. (1999) · Journal of Natural Language Processing · doi:10.5715/jnlp.6.5_43 · Cited by 126 · see citing works
Citation recommendation without author supervision
He, Q., Kifer, D., Pei, J., Mitra, P., & Giles, C. L. (2011) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1935826.1935926 · Cited by 124 · see citing works
Identifying Meaningful Citations
Valenzuela, M., Ha, V., & Etzioni, O. (2015) · National Conference on Artificial Intelligence · Cited by 124 · 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
Interpreting maps of science using citation context sentiments: a preliminary investigation
Small, H. G. (2011) · Scientometrics · doi:10.1007/s11192-011-0349-2 · Cited by 112 · see citing works
To better stand on the shoulder of giants
Yan, R., Huang, C., Tang, J., Zhang, Y., & Li, X. (2012) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/2232817.2232831 · Cited by 110 · see citing works
A classification of citation roles for the social sciences and related fields
Peritz, B. C. (1983) · Scientometrics · doi:10.1007/bf02147226 · Cited by 105 · 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
Comparing citation contexts for information retrieval
Ritchie, A. D., Robertson, S. E., & Teufel, S. (2008) · doi:10.1145/1458082.1458113 · Cited by 94 · see citing works
The invariant distribution of references in scientific articles
Bertin, M., Atanassova, I., Gingras, Y., & Larivière, V. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23367 · Cited by 91 · 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
(2011) · Cited by 84 · see citing works
Can Scientific Impact Be Predicted?
Dong, Y., Johnson, R. A., & Chawla, N. V. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2521657 · Cited by 84 · see citing works
Cascaded Grammatical Relation Assignment
Buchholz, S., Veenstra, J., & Daelemans, W. M. P. (1999) · Research portal (Tilburg University) · doi:10.48550/arxiv.cs/9906004 · Cited by 74 · see citing works
Hu, Z., Chen, C., & Liu, Z. (2013) · Journal of Informetrics · doi:10.1016/j.joi.2013.08.005 · Cited by 74 · see citing works
Skelton, J. (1994) · PubMed · Cited by 74 · see citing works
Wan, X., & Liu, F. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23083 · Cited by 68 · see citing works
Shi, X., Leskovec, J., & McFarland, D. A. (2010) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/1816123.1816131 · Cited by 67 · see citing works
Self-citationTowards a stratified learning approach to predict future citation counts
Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., & Mukherjee, A. (2014) · Cited by 52 · see citing works
Self-citationPredicting a Scientific Community’s Response to an Article
Yogatama, D., Heilman, M., O’Connor, B. T., Dyer, C., Routledge, B., & Smith, N. A. (2011) · KiltHub Repository · Cited by 51 · see citing works
High-Ranked Social Science Journal Articles Can Be Identified from Early Citation Information
Stern, D. I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112520 · Cited by 51 · see citing works
Towards a Computational History of the ACL: 1980-2008
Anderson, A., Jurafsky, D., & McFarland, D. A. (2012) · Cited by 46 · see citing works
Self-citationA 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
Vinkler, P. (1998) · Scientometrics · doi:10.1007/bf02458400 · Cited by 41 · see citing works
Context sensitive topic models for author influence in document networks
Kataria, S., Mitra, P., Caragea, C., & Giles, C. L. (2011) · doi:10.5591/978-1-57735-516-8/ijcai11-379 · Cited by 37 · see citing works
Sentiment analysis of scientific citations
Athar, A. (2021) · CL Technical Reports · doi:10.48456/tr-856 · Cited by 37 · see citing works
Measuring Paradigmaticness of Disciplines Using Text
Evans, E., Gomez, C., & McFarland, D. A. (2016) · Sociological Science · doi:10.15195/v3.a32 · Cited by 36 · see citing works
Self-citationThe Frequency of Hedging Cues in Citation Contexts in Scientific Writing
Mercer, R. E., Marco, C. D., & Kroon, F. W. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24840-8_6 · Cited by 25 · see citing works
A parsing algorithm for unification grammar
Haas, A. R. (1989) · Cited by 24 · see citing works
Structure and Dynamics of Signed Citation Networks
Kumar, S. (2016) · doi:10.1145/2872518.2889391 · Cited by 21 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 8
- Amjad Abu-Jbara and Dragomir Radev. 2012. Reference scope identification in citing sentences. In Proceed- ings of the 2012 Conference of the North American Chapter of the Association for Computational Linguis- tics: Human Language Technologies (NAACL-HLT), pages 80-90. Association for Computational Linguis- tics.
- Awais Athar and Simone Teufel. 2012. Context- enhanced citation sentiment detection. In Proceedings of the 2012 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, pages 597-601. As- sociation for Computational Linguistics.
- Steven Bird, Robert Dale, Bonnie J. Dorr, Bryan R. Gib- son, Mark Joseph, Min-Yen Kan, Dongwon Lee, Brett Powley, Dragomir R. Radev, and Yee Fan Tan. 2008. The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics. In Proceedings of International Confer- ence on Language Resources and Evaluation (LREC).
- Hal Daumé III. 2016. Workshops and mini-conferences, November. https: //nlpers.blogspot.com/2016/11/ workshops-and-mini-conferences.html.
- Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, and Dinani Amorim. 2014. Do we need hun- dreds of classifiers to solve real world classification problems? Journal of Machine Learning Research (JMLR), 15(1):3133-3181.
- John J. Gumperz. 1982. Discourse strategies, volume 1. Cambridge University Press.
- Ellen Riloff and Rosie Jones. 1999. Learning dictionar- ies for information extraction by multi-level bootstrap- ping. In Proceedings of Association for the Advance- ment of Artificial Intelligence (AAAI), pages 474-479.
- John M. Ziman. 1968. Public Knowledge: An Essay Concerning the Social Dimensions of Science. Cam- bridge University Press, Cambridge, UK.
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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
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.740 · Cited by 2,103 · see citing works
The Diversity–Innovation Paradox in Science
Hofstra, B., Kulkarni, V., Galvez, S. M.-N., He, B. D., Jurafsky, D., & McFarland, D. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915378117 · Cited by 1,000 · see citing works
Self-citationS2ORC: 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
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 · see citing works
(2021) · doi:10.1145/3462757.3466088 · Cited by 151 · see citing works
How status of research papers affects the way they are read and cited
Teplitskiy, M., Duede, E., Menietti, M., & Lakhani, K. R. (2022) · Research Policy · doi:10.1016/j.respol.2022.104484 · Cited by 136 · see citing works
Scientific document summarization via citation contextualization and scientific discourse
Cohan, A., & Goharian, N. (2017) · International Journal on Digital Libraries · doi:10.1007/s00799-017-0216-8 · Cited by 100 · see citing works
SciREX: A Challenge Dataset for Document-Level Information Extraction
Jain, S., van Zuylen, M., Hajishirzi, H., & Beltagy, I. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.670 · Cited by 159 · see citing works
New directions in science emerge from disconnection and discord
Lin, Y., Evans, J. A., & Wu, L. (2021) · Journal of Informetrics · doi:10.1016/j.joi.2021.101234 · Cited by 80 · see citing works
Counterfactual Inference for Text Classification Debiasing
Chen, Q., Feng, F., Wen, L., Ma, C., & Xie, P. (2021) · doi:10.18653/v1/2021.acl-long.422 · Cited by 78 · see citing works
Hou, Y., Jochim, C., Gleize, M., Bonin, F., & Ganguly, D. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1513 · Cited by 67 · see citing works
Creative Destruction: The Structural Consequences of Scientific Curation
McMahan, P., & McFarland, D. A. (2021) · American Sociological Review · doi:10.1177/0003122421996323 · Cited by 67 · see citing works
Self-citationScientific Text Sentiment Analysis using Machine Learning Techniques
Raza, H., Faizan, M., Hamza, A., Mushtaq, A., & Akhtar, N. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0101222 · Cited by 67 · see citing works
Citation Intent Classification Using Word Embedding
Roman, M., Shahid, A., Khan, S., Koubâa, A., & Yu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3050547 · Cited by 59 · see citing works
What Types of Novelty Are Most Disruptive?
Leahey, E., Lee, J., & Funk, R. J. (2023) · American Sociological Review · doi:10.1177/00031224231168074 · Cited by 59 · see citing works
Domain-Independent Extraction of Scientific Concepts from Research Articles
Brack, A., D’Souza, J., Hoppe, A., Auer, S., & Ewerth, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45439-5_17 · Cited by 56 · see citing works
Wang, Y., & Zhang, C. (2020) · Journal of Informetrics · doi:10.1016/j.joi.2020.101091 · Cited by 44 · see citing works
A meta-analysis of semantic classification of citations
Kunnath, S. N., Herrmannova, D., Pride, D., & Knoth, P. (2021) · Quantitative Science Studies · doi:10.1162/qss_a_00159 · Cited by 43 · see citing works
ELLE: Efficient Lifelong Pre-training for Emerging Data
Qin, Y., Zhang, J., Lin, Y., Liu, Z., Li, P., Sun, M., & Zhou, J. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.220 · Cited by 43 · see citing works
Shi, S., Hu, K., Xie, J., Guo, Y., & Wu, H. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103531 · Cited by 42 · see citing works
Citation Recommendation Using Distributed Representation of Discourse Facets in Scientific Articles
Kobayashi, Y., Shimbo, M., & Matsumoto, Y. (2018) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3197026.3197059 · Cited by 42 · see citing works
End-to-End Construction of NLP Knowledge Graph
Mondal, I., Hou, Y., & Jochim, C. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.165 · Cited by 42 · see citing works
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · arXiv · doi:10.48550/arxiv.1903.10676 · Cited by 41 · see citing works
Understanding the meanings of citations using sentiment, role, and citation function classifications
Budi, I. D., & Yaniasih, Y. (2022) · Scientometrics · doi:10.1007/s11192-022-04567-4 · Cited by 41 · see citing works
Aljohani, N. R., Fayoumi, A. G., & Hassan, S. (2021) · Journal of Information Science · doi:10.1177/0165551521991022 · Cited by 41 · see citing works
Metrics and mechanisms: Measuring the unmeasurable in the science of science
Wu, L., Kittur, A., Youn, H., Milojević, S., Leahey, E., Fiore, S. M., & Ahn, Y. (2022) · Journal of Informetrics · doi:10.1016/j.joi.2022.101290 · Cited by 41 · see citing works
Several Categories of Large Language Models (LLMs): A Short Survey
Pahune, S. A., & Chandrasekharan, M. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.54677 · Cited by 40 · see citing works
Automatic Classification of Algorithm Citation Functions in Scientific Literature
Tuarob, S., Kang, S. W., Wettayakorn, P., Pornprasit, C., Sachati, T., Hassan, S., & Haddawy, P. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2913376 · Cited by 39 · see citing works
Efficient Domain Adaptation of Language Models via Adaptive Tokenization
Sachidananda, V., Kessler, J. S., & Lai, Y.-A. (2021) · Workshop on Simple and Efficient Natural Language Processing (SustaiNLP) · doi:10.18653/v1/2021.sustainlp-1.16 · Cited by 39 · 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
Self-citationS2ORC: The Semantic Scholar Open Research CorpusPreprint
Lo, K., Wang, L. L., Neumann, M., Kinney, R., & Weld, D. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02782 · Cited by 36 · see citing works
Continual Training of Language Models for Few-Shot Learning
Ke, Z., Lin, H., Shao, Y., Xu, H., Shu, L., & Liu, B. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.695 · Cited by 35 · see citing works
Utilizing BERT for biomedical and clinical text mining
Zhu, R., Tu, X., & Huang, J. X. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819314-3.00005-7 · Cited by 34 · see citing works
Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains
Yao, Y., Huang, S., Wang, W., Dong, L., & Wei, F. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.40 · Cited by 34 · see citing works
Knowledge Inheritance for Pre-trained Language Models
Qin, Y., Lin, Y., Jing, Y., Zhang, J., Han, X., Zhang, Z., Su, Y., Liu, Z., Li, P., Sun, M., et al. (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.288 · 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
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation
Diao, S., Xu, R., Su, H., Jiang, Y., Song, Y., & Zhang, T. (2021) · doi:10.18653/v1/2021.acl-long.259 · Cited by 32 · see citing works
How New Ideas Diffuse in Science
Cheng, M., Smith, D. S., Ren, X., Cao, H., Smith, S., & McFarland, D. A. (2023) · American Sociological Review · doi:10.1177/00031224231166955 · Cited by 32 · see citing works
Self-citationMapping the carbon finance research: A bibliometric exploration of innovation in sustainable finance
Roshid, M. M., Karim, R., Waaje, A., Islam, S., & Bhowmik, R. C. (2025) · Development and sustainability in economics and finance. · doi:10.1016/j.dsef.2025.100048 · Cited by 31 · 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
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora
Jin, X., Zhang, D., Zhu, H., Xiao, W., Li, S.-W. D., Wei, X., Arnold, A., & Ren, X. (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.351 · Cited by 31 · see citing works
Zhao, H., Luo, Z., Feng, C., Zheng, A., & Liu, X. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1524 · Cited by 29 · see citing works
Towards employing native information in citation function classification
Zhang, Y., Zhao, R., Wang, Y., Chen, H., Mahmood, A., Zaib, M., Zhang, W. E., & Sheng, Q. Z. (2022) · Scientometrics · doi:10.1007/s11192-021-04242-0 · Cited by 28 · see citing works
Exploring the applicability of large language models to citation context analysis
Nishikawa, K., & Koshiba, H. (2024) · Scientometrics · doi:10.1007/s11192-024-05142-9 · Cited by 26 · see citing works
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora
Jin, X., Zhang, D., Zhu, H., Xiao, W., Li, S.-W. D., Wei, X., Arnold, A., & Ren, X. (2022) · doi:10.18653/v1/2022.bigscience-1.1 · Cited by 26 · see citing works
Quantifying the rise and fall of scientific fields
Singh, C. K., Barme, E., Ward, R., Tupikina, L., & Santolini, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0270131 · Cited by 25 · 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
Identifying Referential Intention with Heterogeneous Contexts
Yu, W., Yu, M., Zhao, T., & Jiang, M. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380175 · Cited by 24 · see citing works
Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation
Lauscher, A., Wachsmuth, H., Gurevych, I., & Glavaš, G. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00525 · Cited by 24 · see citing works
Explaining Relationships Between Scientific Documents
Luu, K., Wu, X., Koncel-Kedziorski, R., Lo, K., Cachola, I. A., & Smith, N. A. (2021) · doi:10.18653/v1/2021.acl-long.166 · Cited by 24 · see citing works
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
Hong, J., Kim, T., Lim, H., & Choo, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.385 · Cited by 24 · see citing works
CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding
Wright, D., & Augenstein, I. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.157 · Cited by 24 · see citing works
Deep learning meets bibliometrics: A survey of citation function classification
Zhang, Y., Wang, Y., Sheng, Q. Z., Yao, L., Chen, H., Wang, K., Mahmood, A., Zhang, W. E., Zaib, M., Sagar, S., et al. (2024) · Journal of Informetrics · doi:10.1016/j.joi.2024.101608 · Cited by 23 · see citing works
Structure-inducing pre-training
McDermott, M. B. A., Yap, B., Szolovits, P., & Žitnik, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00647-z · Cited by 23 · see citing works
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient FrameworkPreprint
Yao, X., Zheng, Y., Yang, X., & Yang, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.04130 · Cited by 23 · see citing works
The use of citation context to detect the evolution of research topics: a large-scale analysis
Jebari, C., Herrera‐Viedma, E., & Cobo, M. J. (2021) · Scientometrics · doi:10.1007/s11192-020-03858-y · Cited by 21 · 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
Multi-task learning model for citation intent classification in scientific publications
Qi, R., Jia, W., Shao, Z., Li, Z.-G., Chen, H., Sun, Y., & Li, S. (2023) · Scientometrics · doi:10.1007/s11192-023-04858-4 · 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
TDM-CFC: Towards Document-Level Multi-label Citation Function Classification
Zhang, Y., Wang, Y., Sheng, Q. Z., Mahmood, A., Zhang, W. E., & Zhao, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91560-5_26 · Cited by 19 · see citing works
GraphCite: Citation Intent Classification in Scientific Publications via Graph Embeddings
Berrebbi, D., Huynh, N., & Balalau, O. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524657 · Cited by 18 · see citing works
Runhui, L., Li, Y., Ze, J., Qiqi, X., & Chen, X. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103933 · Cited by 18 · see citing works
Prester, J., Wagner, G., Schryen, G., & Hassan, N. R. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113432 · Cited by 17 · see citing works
Healthcare: A Growing Role for Large Language Models and Generative AI
Pahune, S. A., & Rewatkar, N. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.55573 · Cited by 17 · see citing works
Woo, S., & Walsh, J. P. (2024) · Quantitative Science Studies · doi:10.1162/qss_a_00303 · Cited by 16 · see citing works
A simulation-based analysis of the impact of rhetorical citations in science
Bao, H., & Teplitskiy, M. (2024) · Nature Communications · doi:10.1038/s41467-023-44249-0 · Cited by 16 · see citing works
How Data Scientists Review the Scholarly Literature
Mysore, S., Jasim, M., Song, H., Akbar, S., Randall, A. K. C., & Mahyar, N. (2023) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3576840.3578309 · Cited by 16 · see citing works
Self-supervised Regularization for Text Classification
Zhou, M., Li, Z., & Xie, P. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00389 · Cited by 14 · see citing works
Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications
Lucy, L., Dodge, J., Bamman, D., & Keith, K. A. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.433 · Cited by 14 · see citing works
Beyond networks: Aligning qualitative and computational science studies
Cambrosio, A., Cointet, J., & Abdo, A. H. (2020) · Quantitative Science Studies · doi:10.1162/qss_a_00055 · Cited by 14 · see citing works
Wittek, M., Bartenhagen, C., & Berthold, F. (2022) · Social Networks · doi:10.1016/j.socnet.2022.05.001 · Cited by 14 · see citing works
Aljohani, N. R., Fayoumi, A. G., & Hassan, S. (2021) · Applied Sciences · doi:10.3390/app112210970 · Cited by 13 · see citing works
Zheng, A., Zhao, H., Luo, Z., Feng, C., Liu, X., & Ye, Y. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102638 · Cited by 13 · see citing works
Dattolo, A., & Corbatto, M. (2021) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24578 · Cited by 12 · 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
An End-to-End Contrastive Self-Supervised Learning Framework for Language Understanding
Fang, H., & Xie, P. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00521 · Cited by 12 · see citing works
Task Compass: Scaling Multi-task Pre-training with Task Prefix
Zhang, Z., Wang, S., Yi‐chong, X., Fang, Y., Yu, W., Liu, Y., Zhao, H. Y., Zhu, C., & Zeng, M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.416 · Cited by 12 · see citing works
Citation Function Classification Based on Ontologies and Convolutional Neural Networks
Bakhti, K., Niu, Z., Yousif, A., & Nyamawe, A. S. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-95522-3_10 · Cited by 12 · see citing works
A Context-based Framework for Resource Citation Classification in Scientific Literatures
Zhao, H., Luo, Z., Feng, C., & Ye, Y. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331348 · Cited by 12 · see citing works
Koloveas, P., Chatzopoulos, S., Vergoulis, T., & Tryfonopoulos, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05409-8_13 · Cited by 11 · see citing works
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Model
Wang, X., Zhou, W., Zhang, Q., Zhou, J., Gao, S., Wang, J., Zhang, M., Gao, X., Chen, Y. W., & Gui, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.35 · Cited by 11 · see citing works
CiteBench: A Benchmark for Scientific Citation Text Generation
Funkquist, M., Kuznetsov, I., Hou, Y., & Gurevych, I. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.455 · Cited by 11 · see citing works
Yan, E., Chen, Z., & Li, K. (2020) · Quantitative Science Studies · doi:10.1162/qss_a_00040 · Cited by 11 · 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
Ghosal, T., Varanasi, K. K., & Kordoni, V. (2023) · Scientometrics · doi:10.1007/s11192-023-04811-5 · Cited by 10 · see citing works
A New Scheme for Citation Classification based on Convolutional Neural Networks
Bakhti, K., Niu, Z., & Nyamawe, A. S. (2018) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2018-141 · Cited by 10 · see citing works
Sentiment and intent classification of in-text citations using BERT
Visser, R., & Dunaiski, M. (2022) · EPiC series in computing · doi:10.29007/wk21 · Cited by 10 · see citing works
Hybrid Data Augmentation for Citation Function Classification
Zhang, Y., Wang, Y., Sheng, Q. Z., Mahmood, A., Zhang, W. E., & Zhao, R. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191695 · 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
Roman, M., Shahid, A., Uddin, M. I., Hua, Q., & Maqsood, S. (2021) · Complexity · doi:10.1155/2021/5554874 · Cited by 10 · 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
Scientific document processing: challenges for modern learning methods
Kashyap, A. R., Yang, Y., & Kan, M. (2023) · International Journal on Digital Libraries · doi:10.1007/s00799-023-00352-7 · Cited by 9 · see citing works
Opinion Mining by Convolutional Neural Networks for Maximizing Discoverability of Nanomaterials
Xie, T., Wan, Y., Wang, H., Østrøm, I., Wang, S., He, M., Deng, R. D., Wu, X., Grazian, C., Kit, C., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00746 · Cited by 9 · see citing works
Wang, Y., Yi, X., & Zhang, C. (2024) · Journal of Informetrics · doi:10.1016/j.joi.2024.101550 · Cited by 9 · see citing works
Ma, Y., Qing, L., Kang, Y., Liu, J., Zhang, Y., Cheng, Q., Lu, W., & Liu, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127226 · Cited by 9 · see citing works
Keyphrase Extraction in Scholarly Digital Library Search Engines
Patel, K., Caragea, C., Wu, J. F., & Giles, C. L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59618-7_12 · Cited by 9 · see citing works
Studying the Evolution of Scientific Topics and their Relationships
Uban, A. S., Caragea, C., & Dinu, L. P. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.167 · Cited by 9 · see citing works
An empirical study of the design choices for local citation recommendation systems
Medić, Z., & Šnajder, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116852 · Cited by 9 · see citing works
Pre-trained transformer-based citation context-aware citation network embeddings
Ohagi, M., & Aizawa, A. (2022) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3529372.3533290 · Cited by 9 · see citing works
- Authors
- Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D.
- Affiliations
- University of Michigan (US)
- Stanford University (US)
- Stanford University,
- Corresponding author
- Jurgens, David
- Kumar, Srijan
- Hoover, Raine
- Jurafsky, Dan
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press
- Published
- 2018-12
- Volume
- 6
- Pages
- 391-406
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W2808556605
- Cited by
- 212
- References
- 79
- Citations by year
- 2026: 16 · 2025: 25 · 2024: 21 · 2023: 42 · 2022: 34 · 2021: 43 · 2020: 19 · 2019: 12
- FWCI
- 14.11
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (1633036)
- Brown Institute for Media Innovation
- 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 › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Keywords
- citation functions, citation analysis, citation counts, field evolution, publication venues, discourse structure, citation classification, natural language processing, disciplinary consensus
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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