Self-training and co-training in biomedical word sense disambiguation

Word sense disambiguation (WSD) is an intermediate task within information retrieval and information extraction, attempting to select the proper sense of ambiguous words. Due to the scarcity of training data, semi-supervised learning, which profits from seed annotated examples and a large set of unlabeled data, are worth researching. We present preliminary results of two semi-supervised learning algorithms on biomedical word sense disambiguation. Both methods add relevant unlabeled examples to the training set, and optimal parameters are similar for each ambiguous word.