Automated News Suggestions for Populating Wikipedia Entity Pages
Table 4: Number of instances for train and test in the AEP and ASP tasks.
['[EMPTY]', 'F [ITALIC] e train', 'F [ITALIC] e test', 'F [ITALIC] s train', 'F [ITALIC] s test']
We evaluate the generated supervised models for the two tasks, AEP and ASP, by splitting the train and testing instances. It is important to note that for the pairs ⟨n,e⟩ and the triple ⟨n,e,ˆSc⟩, the news article n is referenced at time t by entity e, while the features take into account the entity profile at time t−1. This avoids any ‘overlapping’ content between the news article and the entity page, which could affect the learning task of the functions Fe and Fs. We learn the functions at year t and test on instances for the years greater than t. Please note that we do not show the performance for year 2014 as we do not have data for 2015 for evaluation.