实验室关于生物医学事件触发词检测研究被期刊Neurocomputing录用
新闻来源:IR实验室       发布时间:2019/9/20 0:00:00

  近日,收到期刊《Neurocomputing》编辑部邮件,实验室刁宇峰等的研究工作“FBSN: A Hybrid Fine-grained Neural Network for Biomedical Event Trigger Identification”已被录用。

  摘要:生物医学事件抽取是医学研究和疾病预防的基础性任务之一。事件触发器通常通过使用一个词或短语来表示生物医学事件的发生。此外,生物医学事件触发识别是生物医学事件提取的关键和前提。现有的方法一般依赖于复杂的和无法获得的特征工程。针对这一问题,我们提出了一种混合架构FBSN模型来处理事件触发识别问题,该模型由细粒度双向长短期记忆模型(FBi-LSTM)和支持向量机(SVM)组成。这种混合结构充分利用了模型的优点:FBi-LSTM主要通过细粒度的表示来提取更高层次的特征,而支持向量机主要适用于小数据集的生物医学事件触发词识别任务。然后,我们采用流行的MLEE数据集对该混合结构进行验证。实验结果表明,我们的方法能够达到最先进的基线方法。同时,还对触发器识别任务进行了详细的实验研究。

  Abstract:Biomedical event extraction is one of the fundamental tasks in medical research and disease prevention. Event trigger usually signifies the occurrence of a biomedical event by adopting a word or a phrase. Meanwhile, the task of biomedical event trigger identification is a critical and prerequisite step for biomedical event extraction. The existing methods generally rely on the complex and unobtainable features engineering. To alleviate this problem, we propose a hybrid structure FBSN which consists of Fine-grained Bidirectional Long Short Term Memory (FBi-LSTM) and Support Vector Machine (SVM) to deal with the event trigger identification. The hybrid architecture makes the most of their advantages: FBi-LSTM is to mainly extract the higher level features by the fine-grained representations, and SVM is largely appropriate for small dataset for classifying the results of biomedical event trigger. After that, the popular dataset Multi Level Event Extraction (MLEE) is employed to verify our hybrid structure. Experimental results show that our method is able to achieve the state-of-the-art baseline approaches. Meanwhile, we also discuss the detailed experiments in trigger identification task.