A semi-supervised approach using label propagation to support citation screening
Citation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is...
| Autores: | , , , , , , |
|---|---|
| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/72443 |
| Acesso em linha: | https://hdl.handle.net/10230/72443 http://dx.doi.org/10.1016/j.jbi.2017.06.018 http://hdl.handle.net/10230/72443 |
| Access Level: | acceso abierto |
| Palavra-chave: | Active learning Label propagation Citation screening Semi-supervised learning Text classification |
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A semi-supervised approach using label propagation to support citation screeningKontonatsios, GeorgiosBrockmeier, Austin JayPrzybyla, PiotrMcNaught, JohnMu, TingtingGoulermas, John Y.Ananiadou, SophiaActive learningLabel propagationCitation screeningSemi-supervised learningText classificationCitation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is eligible for inclusion in the review. Recently, several studies have explored the use of active learning in text classification to reduce the human workload involved in the screening task. However, existing approaches require a significant amount of manually labelled citations for the text classification to achieve a robust performance. In this paper, we propose a semi-supervised method that identifies relevant citations as early as possible in the screening process by exploiting the pairwise similarities between labelled and unlabelled citations to improve the classification performance without additional manual labelling effort. Our approach is based on the hypothesis that similar citations share the same label (e.g., if one citation should be included, then other similar citations should be included also). To calculate the similarity between labelled and unlabelled citations we investigate two different feature spaces, namely a bag-of-words and a spectral embedding based on the bag-of-words. The semi-supervised method propagates the classification codes of manually labelled citations to neighbouring unlabelled citations in the feature space. The automatically labelled citations are combined with the manually labelled citations to form an augmented training set. For evaluation purposes, we apply our method to reviews from clinical and public health. The results show that our semi-supervised method with label propagation achieves statistically significant improvements over two state-of-the-art active learning approaches across both clinical and public health reviews.Elsevier2026202620172026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/72443http://dx.doi.org/10.1016/j.jbi.2017.06.018http://hdl.handle.net/10230/72443reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésJournal of Biomedical Informatics. 2017 Aug;72:67-76© 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/724432026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
A semi-supervised approach using label propagation to support citation screening |
| title |
A semi-supervised approach using label propagation to support citation screening |
| spellingShingle |
A semi-supervised approach using label propagation to support citation screening Kontonatsios, Georgios Active learning Label propagation Citation screening Semi-supervised learning Text classification |
| title_short |
A semi-supervised approach using label propagation to support citation screening |
| title_full |
A semi-supervised approach using label propagation to support citation screening |
| title_fullStr |
A semi-supervised approach using label propagation to support citation screening |
| title_full_unstemmed |
A semi-supervised approach using label propagation to support citation screening |
| title_sort |
A semi-supervised approach using label propagation to support citation screening |
| dc.creator.none.fl_str_mv |
Kontonatsios, Georgios Brockmeier, Austin Jay Przybyla, Piotr McNaught, John Mu, Tingting Goulermas, John Y. Ananiadou, Sophia |
| author |
Kontonatsios, Georgios |
| author_facet |
Kontonatsios, Georgios Brockmeier, Austin Jay Przybyla, Piotr McNaught, John Mu, Tingting Goulermas, John Y. Ananiadou, Sophia |
| author_role |
author |
| author2 |
Brockmeier, Austin Jay Przybyla, Piotr McNaught, John Mu, Tingting Goulermas, John Y. Ananiadou, Sophia |
| author2_role |
author author author author author author |
| dc.subject.none.fl_str_mv |
Active learning Label propagation Citation screening Semi-supervised learning Text classification |
| topic |
Active learning Label propagation Citation screening Semi-supervised learning Text classification |
| description |
Citation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is eligible for inclusion in the review. Recently, several studies have explored the use of active learning in text classification to reduce the human workload involved in the screening task. However, existing approaches require a significant amount of manually labelled citations for the text classification to achieve a robust performance. In this paper, we propose a semi-supervised method that identifies relevant citations as early as possible in the screening process by exploiting the pairwise similarities between labelled and unlabelled citations to improve the classification performance without additional manual labelling effort. Our approach is based on the hypothesis that similar citations share the same label (e.g., if one citation should be included, then other similar citations should be included also). To calculate the similarity between labelled and unlabelled citations we investigate two different feature spaces, namely a bag-of-words and a spectral embedding based on the bag-of-words. The semi-supervised method propagates the classification codes of manually labelled citations to neighbouring unlabelled citations in the feature space. The automatically labelled citations are combined with the manually labelled citations to form an augmented training set. For evaluation purposes, we apply our method to reviews from clinical and public health. The results show that our semi-supervised method with label propagation achieves statistically significant improvements over two state-of-the-art active learning approaches across both clinical and public health reviews. |
| publishDate |
2017 |
| dc.date.none.fl_str_mv |
2017 2026 2026 2026 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/10230/72443 http://dx.doi.org/10.1016/j.jbi.2017.06.018 http://hdl.handle.net/10230/72443 |
| url |
https://hdl.handle.net/10230/72443 http://dx.doi.org/10.1016/j.jbi.2017.06.018 http://hdl.handle.net/10230/72443 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Journal of Biomedical Informatics. 2017 Aug;72:67-76 |
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http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf application/pdf |
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Elsevier |
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Elsevier |
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reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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