Table 1 shows the micro and macro averages of F1 scores .

Moreover, the macro-averaged F1 scores clearly showed improvements resulting from using role groups.

In Table 2, we show that the micro-averaged F1 score for roles having 10 instances or less was improved (by 15.46 points) when all role groups were used.

The MUC scoring algorithm (Vilain et a1., 1995) computes the F1 score (harmonic mean) of precision and recall based on the identifcation of unique coreference links.

Precision and recall for a set of documents are computed as the mean over all CEs in the documents and the F1 score of precision and recall is reported.

We then count the number of unique correct/incorrect links that the system introduced on top of the correct partial clustering and compute precision, recall, and F1 score .