  In this paper we propose a new document classification method, bridging
discrepancies (so-called semantic gap) between the training set and the
application sets of textual data. We demonstrate its superiority over classical
text classification approaches, including traditional classifier ensembles. The
method consists in combining a document categorization technique with a single
classifier or a classifier ensemble (SEMCOM algorithm - Committee with Semantic
Categorizer).
