Evaluación de Reglas de Asociación en Text Mining Utilizando ...

Summary
Traditional tasks for text mining discover unseen patterns which are supposed to be
interesting from a statistical point of view by using standard metrics. This evaluation's
statistical nature does not take into account mental models, users or even domain knowledge.
While there is a few approaches to assess patterns (i.e., association rules) in texts mining these
fail to accurately determine whether the discovered patterns are interesting/novel. In this
paper, a new approach which combines corpus based semantics (i.e., LSA) and lattices based
knowledge models is proposed and discussed. Experiments using our approach and
comparisons with state-of-the-art evaluation metrics show the promise of the method with
regard to discovered patterns as interesting or uninteresting to human experts.
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