Automated News Suggestions for Populating Wikipedia Entity Pages
Table 5: Article–Entity placement task performance.
['year', 'P  [BOLD] B1', 'P F [ITALIC] e', 'R  [BOLD] B1', 'R F [ITALIC] e', 'F1  [BOLD] B1', 'F1 F [ITALIC] e']
It is evident from Figure We measure the significance through the t-test statistic and get a p-value of 2.2e−16. The improvement we achieve over B1 in absolute numbers, ΔP=+0.5 in terms of precision for the years between 2009 and 2014, and a similar improvement in terms of F1 score. The improvement for recall is Δ R=+0.4. The relative improvement over B1 for P and F1 is almost 1.8 times better, while for recall we are 3.5 times better. Finally, for B2 we achieve much poorer performance, with average scores of P=0.21, R=0.20 and F1=0.21. Robustness. An interesting observation we make is that we have a very robust performance and the results are stable across the years. If we consider the experimental setup, where for year t=2009 we optimize the learning objective with only 74k training instances and evaluate on the rest of the instances, it achieves a very good performance. We predict with F1=0.68 the remaining 469k instances for the years t∈(2009,2014].