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This article analyses and evaluates FDD\b{eta}, a supervised term-weightingscheme that can be applied for query-term selection in topic-based retrieval.FDD\b{eta} weights terms based on two factors representing the descriptive anddiscriminating power of the terms with respect to the given topic. It thencombines these two factor through the use of an adjustable parameter thatallows to favor different aspects of retrieval, such as precision, recall or abalance between both. The article makes the following contributions: (1) itpresents an extensive analysis of the behavior of FDD\b{eta} as a function ofits adjustable parameter; (2) it compares FDD\b{eta} against eighteentraditional and state-of-the-art weighting scheme; (3) it evaluates theperformance of disjunctive queries built by combining terms selected using theanalyzed methods; (4) it introduces a new public data set with news labeled asrelevant or irrelevant to the economic domain. The analysis and evaluations areperformed on three data sets: two well-known text data sets, namely 20Newsgroups and Reuters-21578, and the newly released data set. It is possibleto conclude that despite its simplicity, FDD\b{eta} is competitive withstate-of-the-art methods and has the important advantage of offeringflexibility at the moment of adapting to specific task goals. The results alsodemonstrate that FDD\b{eta} offers a useful mechanism to explore differentapproaches to build complex queries.

Assessing the behavior and performance of a supervised term-weighting technique for topic-based retrieval