For instance, it is possible to represent anaphoricity as a real-valued feature indicating the probability of an NP being anaphoric rather than as a binary-valued feature.
for training globally-optimized anaphoricity models, RIPPER is always used in conjunction with Method 1 and Max-Ent with Method 2, as described in Section 2.2.