Boosting the protein name recognition performance by bootstrapping on selected text
Yue Wang, Jin-Dong Kim · North American Chapter of the Association for Computational Linguistics · 2012
When only a small amount of manually annotated data is available, application of a bootstrapping method is often considered to compensate for the lack of sufficient training material for a machine-learning method. The paper reports a series of experimental results of bootstrapping for protein name recognition. The results show that the performance changes significantly according to the choice of text collection where the training samples to bootstrap, and that an improvement can be obtained only with a well chosen text collection.