Evaluation of cutoff policies for term extraction
Lucelene Lopes, Renata La Rocca Vieira · Journal of the Brazilian Computer Society · 2015
This paper presents a policy to choose cutoff points to identify potentially relevant terms in a given domain. Term extraction methods usually generate term lists ordered according to a relevance criteria, and the literature is abundant to offer different relevance indices. However, very few studies turn their attention to how many terms should be kept, i.e., to a cutoff policy. Our proposed policy provides an estimation of the portion of this list which preserves a good balance between recall and precision, adopting a refined term extraction and tf-dcf relevance index. A practical study was conducted based on terms extracted from a Brazilian Portuguese corpus, and the results were quantitatively analyzed according to a previously defined reference list. Even thou different extraction procedures and different relevance indices could brought a different outcome, our policy seems to deliver a good balance for the method adopted in our experiments and it is likely to be able to be generalized to other methods.