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A visual attention-based keyword extraction for document classification

机译：基于视觉注意的关键词提取，用于文档分类

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摘要

Document classification plays an important role in natural language processing. Among that, keyword extraction algorithm shows its great potential in summarizing the entire document. Attention is the process of selectively concentrating on a discrete aspect of information, while ignoring other perceivable information. A new probabilistic keyword extraction algorithm is proposed, which is inspired by the visual attention mechanism. An unsupervised neural network based pre-training method is proposed for training the semantic attention based keyword extraction algorithm, which is helpful in extracting keywords with rich contextual information from the document. A bidirectional Long short-term memory network combined with the proposed semantic keyword extraction algorithm is designed for both topic and sentiment classification tasks. Experiments on four large scale datasets show that the proposed visual attention based keyword extraction algorithm gives a better performance than the baseline methods. The semantic attention based keyword extraction method is significant in summarizing the content of a document, which is very useful for large scale document classification.
机译：文档分类在自然语言处理中起着重要作用。其中，关键词提取算法在总结整个文档方面显示出巨大的潜力。注意是选择性地专注于信息的离散方面，而忽略了其他可感知信息的过程。在视觉注意力机制的启发下，提出了一种新的概率关键字提取算法。提出了一种基于无监督神经网络的预训练方法，用于训练基于语义注意的关键词提取算法，该方法有助于从文档中提取具有丰富上下文信息的关键词。针对主题和情感分类任务设计了双向双向长短期记忆网络，并结合了所提出的语义关键词提取算法。在四个大型数据集上的实验表明，所提出的基于视觉注意力的关键词提取算法比基线方法具有更好的性能。基于语义注意力的关键词提取方法在总结文档内容方面具有重要意义，这对于大规模文档分类非常有用。

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