To improve pruning (and hence the precision of mention detection), we employ learning-based pruning, where we employ the training data to identify and subsequently discard those candidate mentions that are not likely to be coreferent with other mentions.
for each mention m k in the test set that survives heuristic pruning, we compute its mention coreference probability, which indicates the likelihood that the head noun of m k is coreferent with another mention.