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Cited by 12Computational and Text Analysis MethodsMeta-analysis and systematic reviewsAdvanced Text Analysis Techniques

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

slr-kit: A semi-supervised machine learning framework for systematic literature reviews

Facchinetti, Tullio, Benetti, Guido, Giuffrida, Davide, Nocera, Antonino·2022·Knowledge-Based Systems
DOI 10.1016/j.knosys.2022.109266
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Crossref · 10.1016/j.knosys.2022.109266

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Citations

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Research works printed but not linked: 20

  1. Mulrow, 1994, Systematic reviews: rationale for systematic reviews, Bmj, 309, 597, 10.1136/bmj.309.6954.597 · DOI · Paper page
  2. Kobayashi, 2000, Information retrieval on the web, ACM Comput. Surv., 32, 144, 10.1145/358923.358934 · DOI
  3. Jiang, 2020, Semantically-enhanced information retrieval using multiple knowledge sources, Cluster Comput., 23, 2925, 10.1007/s10586-020-03057-7 · DOI
  4. Nasar, 2018, Information extraction from scientific articles: a survey, Scientometrics, 117, 1931, 10.1007/s11192-018-2921-5 · DOI
  5. Pulsiri, 2018, Improving systematic literature review with automation and bibliometrics, 1
  6. Olorisade, 2017, Reproducibility of studies on text mining for citation screening in systematic reviews: evaluation and checklist, J. Biomed. Inform., 73, 1, 10.1016/j.jbi.2017.07.010 · DOI · Paper page
  7. Molléri, 2012, Automated approaches to support secondary study processes: a systematic review, 143
  8. Ananiadou, 2009, Supporting systematic reviews using text mining, Soc. Sci. Comput. Rev., 27, 509, 10.1177/0894439309332293 · DOI
  9. Sasaki, 2009, Clinical text classification under the open and closed topic assumptions, Int. J. Data Min. Bioinform., 3, 299, 10.1504/IJDMB.2009.026703 · DOI
  10. Jonnalagadda, 2013, A new iterative method to reduce workload in systematic review process, Int. J. Comput. Biol. Drug Des., 6, 5, 10.1504/IJCBDD.2013.052198 · DOI · Paper page
  11. Miwa, 2014, Reducing systematic review workload through certainty-based screening, J. Biomed. Inform., 51, 242, 10.1016/j.jbi.2014.06.005 · DOI · Paper page
  12. Hashimoto, 2016, Topic detection using paragraph vectors to support active learning in systematic reviews, J. Biomed. Inform., 62, 59, 10.1016/j.jbi.2016.06.001 · DOI
  13. Kim, 2019, Research paper classification systems based on TF-IDF and LDA schemes, Human-Centric Comput. Inf. Sci., 30
  14. Blei, 2003, Latent dirichlet allocation, J. Mach. Learn. Res., 3, 993
  15. Watanabe, 2020, Reducing efforts of software engineering systematic literature reviews updates using text classification, Inf. Softw. Technol., 128, 10.1016/j.infsof.2020.106395 · DOI
  16. Yates, 2009, Unsupervised methods for determining object and relation synonyms on the web, J. Artificial Intelligence Res., 34, 255, 10.1613/jair.2772 · DOI
  17. Kris Heylen, Yves Peirsman, Dirk Geeraerts, Dirk Speelman, Modelling word similarity: an evaluation of automatic synonymy extraction algorithms, in: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), 2008.
  18. Havrlant, 2017, A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and variations motivated by this explanation), Int. J. Gen. Syst., 46, 27, 10.1080/03081079.2017.1291635 · DOI
  19. Diamantini, 2020, Querying the iot using multiresolution contexts, IEEE Internet Things J., 8, 6127, 10.1109/JIOT.2020.3033669 · DOI
  20. Einat Amitay, Nadav Har’El, Ron Sivan, Aya Soffer, Web-a-where: geotagging web content, in: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004, pp. 273–280. · DOI

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Metadata

Authors
Facchinetti, T., Benetti, G., Giuffrida, D., & Nocera, A.
Corresponding author
Facchinetti, Tullio
ORCID
Venue
Knowledge-Based Systems
Publisher
Elsevier BV
Published
2022-09
Volume
251
Pages
109266
ISSN
0950-7051 (ISSN-L)
DOI
10.1016/j.knosys.2022.109266
OpenAlex
W4283078815
Cited by
12
References
8
Citations by year
2026: 3 · 2025: 4 · 2024: 4 · 2022: 1
FWCI
0.75
Type
journal article
Language
en
Open access
closed
Licence
Locations
Knowledge-Based Systems (publishedVersion)
Topics
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
Keywords
systematic reviews, semi-supervised learning, literature review automation, topic modeling, latent Dirichlet allocation, topic clustering, publication trends

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CoChat. (2026). Citation check: slr-kit: A semi-supervised machine learning framework for systematic literature reviews. https://app.cochat.ai/papers/10.1016/j.knosys.2022.109266
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Facchinetti, T., Benetti, G., Giuffrida, D., & Nocera, A. (2022). slr-kit: A semi-supervised machine learning framework for systematic literature reviews. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2022.109266