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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks
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Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks

机译：用并行多池卷积神经网络提取生物医学事件

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摘要

Biomedical event extraction is important for medical research and disease prevention, which has attracted much attention in recent years. Traditionally, most of the state-of-the-art systems have been based on shallow machine learning methods, which require many complex, hand-designed features. In addition, the words encoded by one-hot are unable to represent semantic information. Therefore, we utilize dependency-based embeddings to represent words semantically and syntactically. Then, we propose a parallel multi-pooling convolutional neural network (PMCNN) model to capture the compositional semantic features of sentences. Furthermore, we employ a rectified linear unit, which creates sparse representations with true zeros, and which is adapted to the biomedical event extraction, as a nonlinear function in PMCNN architecture. The experimental results from MLEE dataset show that our approach achieves an F1 score of 80.27 percent in trigger identification and an F1 score of 59.65 percent in biomedical event extraction, which performs better than other state-of-the-art methods.
机译：生物医学事件提取对于医学研究和疾病预防是重要的，这近年来引起了很多关注。传统上，大多数最先进的系统都是基于浅机器学习方法，需要许多复杂的手工设计的功能。此外，由一热编码的单词无法表示语义信息。因此，我们利用基于依赖性的嵌入来语义和句法来表示​​单词。然后，我们提出了一个并行多池卷积神经网络（PMCNN）模型，以捕获句子的组成语义特征。此外，我们采用了纠正的线性单元，该单位产生具有真正零的稀疏表示，并且适用于生物医学事件提取，作为PMCNN架构中的非线性函数。 Mlee DataSet的实验结果表明，我们的方法在触发识别中实现了80.27％的F1得分，生物医学事件提取的F1得分为59.65％，这比其他最先进的方法更好。

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