Towards keyphrase assignment for texts in portuguese language

Abstract

Keyphrase assignment has often been confounded with keyphrase extraction, since the basic hypothesis is that a keyphrase of a text must be extracted from this text. Typically, keyphrase extraction approaches use a training set restricted to textual terms, reducing the learning capabilities of any inductive algorithm. Our research investigates ways to improve the accuracy of the keyphrase assignment systems for texts in Portuguese language by allowing classification algorithms to learn from non-textual terms as well. The basic assumption we have followed is that non-textual terms can be included into the training set by inference from an eventual semantic relationship with textual terms. In order to discover the latent relationship between non-textual and textual terms, we use deductive strategies to be applied in Portuguese common sense bases such as Wikipedia and InferenceNet. We show that algorithms that follow our approach outperform others that do not use the same methods introduced here.