Improving Probabilistic Models In Text Classification Via Active Learning.
Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool since it requires less human coding. However, scholars still need many human-labeled documents for training. To red...
| Publicado en: | American Political Science Review Vol. 119; no. 2; pp. 985 - 1003 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Cambridge University Press
May2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=184994654&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 184994654 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00030554 APR jtl: American Political Science Review issn: 00030554 maglogo: N pubinfo: dt: May2025 vid: 119 iid: 2 pid: 15979 pub: Cambridge University Press artinfo: ui: 184994654 10.1017/S0003055424000716 ppf: 985 ppct: 18 formats: tig: atl: Improving Probabilistic Models In Text Classification Via Active Learning. aug: au: BOSLEY, MITCHELL KUZUSHIMA, SAKI ENAMORADO, TED SHIRAITO, YUKI affil: University of Michigan, United States, and University of Toronto, Canada University of Michigan, and Harvard University, United States Washington University in St. Louis, United States University of Michigan, United States su: Social scientists Empirical research Active learning Scholars Classification algorithms Published articles sug: subj: Social scientists Empirical research Active learning Scholars Classification algorithms Published articles ab: Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool since it requires less human coding. However, scholars still need many human-labeled documents for training. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that with few labeled data, the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. We replicate the results of two published articles with only a small fraction of the original labeled data used in those studies and provide open-source software to implement our method. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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