In this paper, we present a multi-featured supervised automatic keyword extraction system. We extracted salient semantic features which are descriptive of candidate keyphrases, a Random Forest classifer was used for training. The system achieved an accuracy of 58.3 % precision and has shown to outperform two top performing systems when benchmarked on a crowdsourced dataset. Furthermore, our approach achieved a personal best Precision and F-measure score of 32.7 and 25.5 respectively on the Semeval Keyphrase extraction challenge dataset. The paper describes the approaches used as well as the result obtained.
SnT, University of Luxembourg
http://wwwde.uni.lu/snt
Adebayo, Kolawole John is a PhD student presently affiliated to the Interdisciplinary Centre for Security, Reliability and Trust (SnT) of the University of Luxembourg under the Erasmus Mundus LAST-JD framework.
