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Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification

机译：多视图卷积神经网络中的异构注意力机制融合文本分类

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摘要

The rapid proliferation of user generated content has given rise to large volumes of text corpora. Increasingly, scholars, researchers, and organizations employ text classification to mine novel insights for high-impact applications. Despite their prevalence, conventional text classification methods rely on labor-intensive feature engineering efforts that are task specific, omit long-term relationships, and are not suitable for the rapidly evolving domains. While an increasing body of deep learning and attention mechanism literature aim to address these issues, extant methods often represent text as a single view and omit multiple sets of features at varying levels of granularity. Recognizing that these issues often result in performance degradations, we propose a novel Spatial View Attention Convolutional Neural Network (SVA-CNN). SVA-CNN leverages an innovative and carefully designed set of multi-view representation learning, a combination of heterogeneous attention mechanisms and CNN-based operations to automatically extract and weight multiple granularities and fine-grained representations. Rigorously evaluating SVA-CNN against prevailing text classification methods on five large-scale benchmark datasets indicates its ability to outperform extant deep learning based classification methods in both performance and training time for document classification, sentiment analysis, and thematic identification applications. To facilitate model reproducibility and extensions, SVA-CNN's source code is also available via GitHub. (c) 2020 Elsevier Inc. All rights reserved.
机译：用户生成的内容的快速扩散导致了大量文本语料库的出现。越来越多的学者、研究人员和组织使用文本分类来挖掘对高影响应用程序的新见解。尽管传统的文本分类方法非常普遍，但它们依赖于劳动密集型的特征工程，这些特征工程是特定于任务的，忽略了长期关系，不适合快速发展的领域。虽然越来越多的深度学习和注意机制文献旨在解决这些问题，但现有的方法通常将文本表示为一个视图，并忽略不同粒度的多组特征。认识到这些问题往往会导致性能下降，我们提出了一种新的空间视图注意卷积神经网络（SVA-CNN）。SVA-CNN利用一套创新且精心设计的多视图表示学习，将异构注意机制和基于CNN的操作相结合，自动提取和加权多粒度和细粒度表示。在五个大型基准数据集上严格评估SVA-CNN与主流文本分类方法的对比表明，SVA-CNN在文档分类、情感分析和主题识别应用的性能和训练时间方面都优于现有的基于深度学习的分类方法。为了促进模型的再现性和扩展，SVA-CNN的源代码也可以通过GitHub获得。（c） 2020爱思唯尔公司版权所有。

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