  \emph{Semi-Automated Text Classification} (SATC) may be defined as the task
of ranking a set $\mathcal{D}$ of automatically labelled textual documents in
such a way that, if a human annotator validates (i.e., inspects and corrects
where appropriate) the documents in a top-ranked portion of $\mathcal{D}$ with
the goal of increasing the overall labelling accuracy of $\mathcal{D}$, the
expected increase is maximized. An obvious SATC strategy is to rank
$\mathcal{D}$ so that the documents that the classifier has labelled with the
lowest confidence are top-ranked. In this work we show that this strategy is
suboptimal. We develop new utility-theoretic ranking methods based on the
notion of \emph{validation gain}, defined as the improvement in classification
effectiveness that would derive by validating a given automatically labelled
document. We also propose a new effectiveness measure for SATC-oriented ranking
methods, based on the expected reduction in classification error brought about
by partially validating a list generated by a given ranking method. We report
the results of experiments showing that, with respect to the baseline method
above, and according to the proposed measure, our utility-theoretic ranking
methods can achieve substantially higher expected reductions in classification
error.
