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首页> 外文期刊>International journal of data mining and bioinformatics >Biomedical named entity recognition based on recurrent neural networks with different extended methods
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Biomedical named entity recognition based on recurrent neural networks with different extended methods

机译：基于递归神经网络的不同扩展方法的生物医学命名实体识别

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摘要

Biomedical Named Entity Recognition (bio-NER) has become essential to the text mining and knowledge discovery tasks in biomedical field. However, the performance of traditional NER systems is limited to the construction of complex hand-designed features which are derived from various linguistic analyses and may only adapted to specified domain. In this paper, we mainly focus on building a simple and efficient system for bio-NER based on Recurrent Neural Network (RNN) where complex hand-designed features are replaced with word embeddings. Furthermore, the system is extended by the predicted information from the prior node and external context information (topical information & clustering information). During the training process, the word embeddings are fine-tuned by the neural network. The experiments conducted on the BioCreative II GM data set demonstrate RNN models outperform CRF model and Deep Neural Networks (DNNs) and the extended RNN model performs better than the original RNN, achieving 82.47% F-score.
机译：生物医学命名实体识别（bio-NER）对于生物医学领域的文本挖掘和知识发现任务已变得至关重要。但是，传统NER系统的性能仅限于复杂的手工设计特征的构造，这些特征是从各种语言分析中得出的，并且只能适应特定领域。在本文中，我们主要致力于基于递归神经网络（RNN）构建简单高效的bio-NER系统，该系统将复杂的手工设计特征替换为词嵌入。此外，通过来自先验节点的预测信息和外部上下文信息（主题信息和聚类信息）扩展了系统。在训练过程中，通过神经网络对单词嵌入进行微调。在BioCreative II GM数据集上进行的实验表明RNN模型优于CRF模型和深度神经网络（DNN），并且扩展RNN模型的性能优于原始RNN，达到了82.47％的F评分。

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