SZTERGAK : Feature Engineering for Keyphrase Extraction
Gábor Berend, Richárd Farkas · Meeting of the Association for Computational Linguistics · 2010
Automatically assigning keyphrases to documents has a great variety of applications. Here we focus on the keyphrase extraction of scientific publications and present a novel set of features for the supervised learning of keyphraseness. Although these features are intended for extracting keyphrases from scientific papers, because of their generality and robustness, they should have uses in other domains as well. With the help of these features SZTERGAK achieved top results on the SemEval-2 shared task on Automatic Keyphrase Extraction from Scientific Articles and exceeded its baseline by 10%.