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论文类型：会议论文

发表时间：2012-01-01

收录刊物：CPCI-S、SCIE

关键字：bio-event extraction; unlabeled data; data sparseness

摘要：In biomedical event extraction domain, there is a small amount of labeled data along with a large pool of unlabeled data. Many supervised learning algorithms for bio-event extraction have been affected by the data sparseness. In this paper, we present a new solution to perform biomedical event extraction from scientific documents, applying a semi-supervised approach to extract features from unlabeled data using labeled data features as a reference. This strategy is evaluated via experiments in which the data from the BioNLP2011 and PubMed are applied. To the best of our knowledge, it is the first time that the combination of labeled and unlabeled data are used for biomedical event extraction and our experimental results demonstrate the state-of-the-art performance in this task.