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.
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. His research generally bothers on the application of Machine Learning and Natural Language Processing techniques to the legal domain as especially related to Information extraction and retrieval, as well as semantic annotation of Legal document. He can be reached at email@example.com and firstname.lastname@example.org