Cancer Hallmark Text Classification Using Convolutional Neural Networks-猫眼课题宝

DOI：

10.17863/cam.12420

发表时间：

2016-12

期刊：

影响因子：

通讯作者：

中科院分区：

其他

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作者：

Simon Baker;A. Korhonen;Sampo Pyysalo

基于深度学习方法的方法最近在一系列机器学习任务中取得了最先进的性能，并且越来越多地应用于自然语言处理（NLP）。尽管在涉及一般领域文本的各种已建立的 NLP 任务中取得了很好的成果，但将这些模型应用于生物医学 NLP 的工作仍然有限。在本文中，我们考虑使用卷积神经网络（CNN）方法进行生物医学文本分类。使用最近引入的癌症领域数据集（涉及根据公认的癌症标志对文档进行分类）进行的评估表明，基本 CNN 模型可以达到与支持向量机 (SVM) 相媲美的性能水平，支持向量机 (SVM) 使用针对任务优化的复杂手动设计特征进行训练。我们进一步表明，对 CNN 超参数、初始化和训练过程进行简单修改，可以使模型显着优于 SVM，从而在此任务中建立新的最先进结果。我们在 https://cambridgeltl.github.io/cancer-hallmark-cnn/ 的开放许可下提供本研究中引入的所有资源和工具。

Methods based on deep learning approaches have recently achieved state-of-the-art performance in a range of machine learning tasks and are increasingly applied to natural language processing (NLP). Despite strong results in various established NLP tasks involving general domain texts, there is only limited work applying these models to biomedical NLP. In this paper, we consider a Convolutional Neural Network (CNN) approach to biomedical text classification. Evaluation using a recently introduced cancer domain dataset involving the categorization of documents according to the well-established hallmarks of cancer shows that a basic CNN model can achieve a level of performance competitive with a Support Vector Machine (SVM) trained using complex manually engineered features optimized to the task. We further show that simple modifications to the CNN hyperparameters, initialization, and training process allow the model to notably outperform the SVM, establishing a new state of the art result at this task. We make all of the resources and tools introduced in this study available under open licenses from https://cambridgeltl.github.io/cancer-hallmark-cnn/.

Cancer Hallmark Text Classification Using Convolutional Neural Networks

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