Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model

Qiuyue Wang, Xiaofeng Meng
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引用次数: 0

Abstract

Automatic recognition of biomedical entities in text is the crucial initial step in biomedical text mining. In this pa-per, we investigate employing modern neural network models for recognizing biomedical entities. To compensate for the small amount of training data in biomedical domain, we propose to integrate dictionaries into the neural model. Our experiments on BB3 data sets demonstrate that state-of-the-art neural network model is promising in recognizing biomedical entities even with very little training data. When integrated with dictionaries, its performance could be greatly improved, achieving the competitive performance compared with the best dictionary-based system on the entities with specific terminology, and much higher performance on the entities with more general terminology.
基于词典增强神经网络模型的细菌和生物群落实体识别
文本中生物医学实体的自动识别是生物医学文本挖掘的关键步骤。本文研究了利用现代神经网络模型识别生物医学实体的方法。为了弥补生物医学领域训练数据较少的不足,我们提出将字典集成到神经模型中。我们在BB3数据集上的实验表明,即使训练数据很少,最先进的神经网络模型在识别生物医学实体方面也很有前景。当与字典集成时,它的性能可以大大提高,在具有特定术语的实体上实现与最佳基于字典的系统相比的竞争性能,并且在具有更通用术语的实体上实现更高的性能。
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