In particular, with this features/dataset combination, with the glm library we see slight rises in Precision@5, F1-Score@15 and MAP from 24% to 24.4%, from 19.22% to 20.30% and from 0.127 o 0.136 respectively; for C5.0, while F1-Score rises from 18.95% to just 19.29%, Precision@5 and MAP shows a more significant improvement from 22.2% to 26.8% and from 0.123 to 0.137 respectively, thus supposedly showing a better ranking of the keyphrases found.
using the same feature set over the preprocessed documents still shows an improvement from the baseline, but with slightly lower scores.