Biblio

Filters: Author is Merlino, Hernán D.  [Clear All Filters]
2019-01-31
Rodríguez, Juan M., Merlino, Hernán D., Pesado, Patricia, García-Martínez, Ramón.  2018.  Evaluation of Open Information Extraction Methods Using Reuters-21578 Database. Proceedings of the 2Nd International Conference on Machine Learning and Soft Computing. :87–92.

The following article shows the precision, the recall and the F1-measure for three knowledge extraction methods under Open Information Extraction paradigm. These methods are: ReVerb, OLLIE and ClausIE. For the calculation of these three measures, a representative sample of Reuters-21578 was used; 103 newswire texts were taken randomly from that database. A big discrepancy was observed, after analyzing the obtained results, between the expected and the observed precision for ClausIE. In order to save the observed gap in ClausIE precision, a simple improvement is proposed for the method. Although the correction improved the precision of Clausie, ReVerb turned out to be the most precise method; however ClausIE is the one with the better F1-measure.