Li Fei, Zhang Meishan, Fu Guohong, Ji Donghong
School of Computer, Wuhan University, Bayi Road, Wuhan, China.
School of Computer Science and Technology, Heilongjiang University, Xuefu Road, Harbin, China.
BMC Bioinformatics. 2017 Mar 31;18(1):198. doi: 10.1186/s12859-017-1609-9.
Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above.
Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction.
The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.
从文本中提取生物医学实体及其关系在生物医学研究中具有重要应用。先前的工作主要利用基于特征的流水线模型来处理此任务。在使用基于特征的模型时，需要在特征工程方面付出很多努力。此外，流水线模型可能会遭受错误传播，并且无法利用子任务之间的交互。因此，我们提出了一种神经联合模型，用于同时提取生物医学实体及其关系，并且它可以缓解上述问题。
我们的模型在两项任务上进行了评估，即提取药物与疾病实体之间的药物不良事件任务，以及提取细菌与位置实体之间的驻留关系任务。与这些任务中的现有系统相比，我们的模型在实体识别中，将第一项任务的F1分数提高了5.1%，在关系提取中提高了8.0%，在关系提取中，将第二项任务的F1分数提高了9.2%。
所提出的模型在特征工程方面工作量较少的情况下实现了具有竞争力的性能。我们证明基于神经网络的模型对于生物医学实体和关系提取是有效的。此外，参数共享是神经模型联合处理此任务的一种替代方法。我们的工作可以促进生物医学文本挖掘的研究。
