I also have my doubts about how appropriate the Precision/Recall-Breakeven Point is
for measuring effectiveness of a text retrieval algorithm. Although it is a common
measure for evaluating text classifiers, as the authors mention, they introduce a
new definition for their algorithm! Or at least they should talk about how the confidence
value for Naive Bayes classifiers/SVM translates to the corresponding measure in TSVM's,
which was just defined as the point where the number of false positives being equal to
number of false negatives.

On a different point, I see a parallel between this paper and the one on boosting image
retrieval in that they both talk about sparse problem instances. And I think that although
it is valid to derive new algorithms for these types of problems, care should be taken as
to the type of benchmarks that are appropriate for testing.