We also conducted a similar analysis on the multi-label predictions, for which the highest-ranked true context were not in the top-3. For such predictions we compared the top-1 prediction to the highestranked true context. The average agreement score is 0.50, indicating that the multi-label prediction is marginally closer to the true context on average. For the ligand and solvent categories, the likelihood of agreement is higher or equal for the multi-label model. These results illustrate that we can potentially improve the models by exploiting multi-label data. The dataset has strong temporal characteristics. We analyzed the time-dependency of context usage, by counting the number of times a context has been used for a particular year as shown in Figure .