Combining labeled and unlabeled data for biomédical event extraction

Jian Wang, Qian Xu, Hongfei Lin, Zhihao Yang, Yanpeng Li
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引用次数: 1

Abstract

In biomédical 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 biomédical 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 biomédical event extraction and our experimental results demonstrate the state-of-the-art performance in this task.
结合标记和未标记数据进行生物医学事件提取
在生物医学事件提取领域,有少量的标记数据和大量的未标记数据。许多生物事件提取的监督学习算法都受到数据稀疏性的影响。在本文中,我们提出了一种新的解决方案,从科学文献中进行生物医学事件提取,采用半监督方法,以标记数据特征为参考,从未标记的数据中提取特征。该策略通过应用BioNLP2011和PubMed的数据进行实验评估。据我们所知,这是第一次将标记和未标记的数据结合用于生物医疗器械事件提取,我们的实验结果显示了在这项任务中的最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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