Table 2.

Article filtering performance with different features and classifiers

Model	Precision	Recall	F1 score	AUC
Mean	0.6642	0.7636	0.6868	0.7351
Standard deviation	0.0810	0.1926	0.1035	0.0741
Best reported in terms of AUC [8]	0.7080	0.8609	0.7770	0.8554
Our results in BioCreative 2006	0.7507	0.8107	0.7795	0.8471
Term (baseline)	0.7016	0.8213	0.7568	0.8037
String	0.7044	0.8960	0.7887	0.8416
Named entity (NE)	0.5815	0.9600	0.7243	0.7570
Template	0.7841	0.7653	0.7746	0.8239
String + NE	0.7360	0.8773	0.8005	0.8479
String + template	0.7416	0.8880	0.8082	0.8372
String + NE + template	0.7585	0.8373	0.7959	0.8507
String + term + NE + template	0.7432	0.8720	0.8025	0.8608
Naïve Bayes classifier	0.6321	0.8613	0.7291	0.7884
Multinomial classifier	0.6264	0.8720	0.7290	0.7770
Linear kernel SVM	0.7016	0.8213	0.7568	0.8037
p-spectrum kernel SVM (p = 7)	0.7352	0.8293	0.7794	0.8376
Integration of the above four classifiers (AdaBoost)	0.7995	0.8933	0.8438	0.8746

This table shows the experimental results from article filtering. AUC, area under the receiving operator characteristic curve; SVM, support vector machine.