An Ensemble Algorithm for Sequential Labelling: A Case Study in Chemical Named Entity Recognition
نویسندگان
چکیده
Ensemble methods are learning algorithms that classify new data points by synthesizing the predictions of a set of classifiers. Many methods for constructing ensembles have been proposed such as weighted voting, manipulations of training samples, features, or labels. The paper proposes a novel ensemble algorithm which constructs ensembles by manipulating the label set given to the learning algorithm and then classifies a new dataset by a voting algorithm specifically designed for sequential labelling task. The dataset released in the BioCreative V.5 CEMP (Chemical Entity Mention recognition) task was used to evaluate the performance of proposed algorithm. The results revealed that the proposed algorithm can improve the precision and F-score.
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