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Fig. 5 | Systematic Reviews

Fig. 5

From: SWIFT-Review: a text-mining workbench for systematic review

Fig. 5

Observed changes in WSS@95 attributable to three feature types. a LDA, b MeSH terms, and c N-grams on 20 SR datasets. Mean changes in WSS@95 were 4.4 % (LDA), 1 % (MeSH), and −0. 4 % (NGrams). In each case, performance was measured on each of the 20 datasets both with and without the specified feature type. The resulting WSS@95 differences for each dataset were averaged over 25 trials. As shown in a, adding LDA features to the ranking algorithm can result in significant performance increases, whereas inclusion of the MeSH and NGram features (b and c) were not found to result in large additional benefits when the remaining feature types were also included

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