Name
Abstract | ||
|---|---|---|
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 classifier 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. |
Year | Venue | Field |
|---|---|---|
2016 | SEMANTICS | Decision tree,Data mining,SemEval,Information retrieval,Computer science,Keyword extraction,Random forest,Salient |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kolawole John Adebayo | 1 | 2 | 1.08 |
| Luigi Di Caro | 2 | 195 | 35.21 |
| Guido Boella | 3 | 1867 | 162.59 |