Mostly, yes. This particular program evaluates effectiveness of features given an existing log-linear model. So, you can evaluate the effectiveness of features in parallel. For each feature, you can also partition the training data, process the the data in parallel, and apply a reduction step[1].

But 'map' in MapReduce is also a typical data-parallel task.

[1] In practice, there is a trade-off: the vectors are usually so large for the average training set, that you do not really want to copy them for memory-efficiency, so the mapping and reduction are interleaved, requiring some locking.