  This article analyses and evaluates FDD\b{eta}, a supervised term-weighting
scheme that can be applied for query-term selection in topic-based retrieval.
FDD\b{eta} weights terms based on two factors representing the descriptive and
discriminating power of the terms with respect to the given topic. It then
combines these two factor through the use of an adjustable parameter that
allows to favor different aspects of retrieval, such as precision, recall or a
balance between both. The article makes the following contributions: (1) it
presents an extensive analysis of the behavior of FDD\b{eta} as a function of
its adjustable parameter; (2) it compares FDD\b{eta} against eighteen
traditional and state-of-the-art weighting scheme; (3) it evaluates the
performance of disjunctive queries built by combining terms selected using the
analyzed methods; (4) it introduces a new public data set with news labeled as
relevant or irrelevant to the economic domain. The analysis and evaluations are
performed on three data sets: two well-known text data sets, namely 20
Newsgroups and Reuters-21578, and the newly released data set. It is possible
to conclude that despite its simplicity, FDD\b{eta} is competitive with
state-of-the-art methods and has the important advantage of offering
flexibility at the moment of adapting to specific task goals. The results also
demonstrate that FDD\b{eta} offers a useful mechanism to explore different
approaches to build complex queries.
