Abstract
This paper describes two classifiers, Naïve Bayes and Support Vector Machine (SVM), to classify sentences containing
Databank Accession Numbers, a key piece of bibliographic information, from online biomedical articles. The correct
identification of these sentences is necessary for the subsequent extraction of these numbers. The classifiers use words
that occur most frequently in sentences as features for the classification. Twelve sets of word features are collected to train
and test the classifiers. Each set has a different number of word features ranging from 100 to 1,200. The performance of
each classifier is evaluated using four measures: Precision, Recall, F-Measure, and Accuracy. The Naïve Bayes classifier
shows performance above 93.91% at 200 word features for all four measures. The SVM shows 98.80% Precision at 200
word features, 94.90% Recall at 500 and 700, 96.46% F-Measure at 200, and 99.14% Accuracy at 200 and 400. To
improve classification performance, we propose two merging operators, Max and Harmonic Mean, to combine results of
the two classifiers. The final results show a measureable improvement in Recall, F-Measure, and Accuracy rates.
