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Deep learning models and datasets for aspect term sentiment classification: Implementing holistic recurrent attention on target-dependent memories

机译：用于方面术语情感分类的深度学习模型和数据集：对依赖于目标的记忆进行整体的周期性关注

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An essential challenge in aspect term sentiment classification using deep learning is modeling a tailormade sentence representation towards given aspect terms to enhance the classification performance. To seek a solution to this, we have two main research questions: (1) Which factors are vital for a sentiment classifier? (2) How will these factors interact with dataset characteristics? Regarding the first question, harmonious combination of location attention and content attention may be crucial to alleviate semantic mismatch problem between aspect terms and opinion words. However, location attention does not reflect the fact that critical opinion words usually come left or right of corresponding aspect terms, as implied in the target-dependent method although not well elucidated before. Besides, content attention needs to be sophisticated to combine multiple attention outcomes nonlinearly and consider the entire context to address complicated sentences. We merge all these significant factors for the first time, and design two models differing a little in the implementation of a few factors. Concerning the second question, we suggest a new multifaceted view on the dataset beyond the current tendency to be somewhat indifferent to the dataset in pursuit of a universal best performer. We then observe the interaction between factors of model architecture and dimensions of dataset characteristics. Experimental results show that our models achieve state-of-the-art or comparable performances and that there exist some useful relationships such as superior performance of bidirectional LSTM over one-directional LSTM for sentences containing multiple aspects and vice versa for sentences containing only one aspect. (C) 2019 Elsevier B.V. All rights reserved.
机译：使用深度学习的方面术语情感分类中的一个基本挑战是，针对给定的方面术语对量身定制的句子表示进行建模，以增强分类性能。为了寻求解决方案，我们有两个主要的研究问题：（1）哪些因素对于情感分类器至关重要？ （2）这些因素将如何与数据集特征相互作用？关于第一个问题，位置注意和内容注意的和谐结合对于缓解方面术语和见解词之间的语义失配问题可能至关重要。但是，位置关注并不能反映评论意见词通常出现在相应方面术语的左侧或右侧，正如目标依赖方法所暗示的那样，尽管之前没有很好地阐明。此外，内容注意需要复杂，以非线性方式组合多个注意结果，并考虑整个上下文以解决复杂的句子。我们第一次将所有这些重要因素合并，并设计了两个模型，其中一些因素的实现略有不同。关于第二个问题，我们建议对数据集提出一种新的多角度的观点，以超越目前的趋势，以寻求通用最佳绩效者而对数据集无动于衷。然后，我们观察模型架构因素与数据集特征维度之间的相互作用。实验结果表明，我们的模型达到了最先进或相当的性能，并且存在一些有用的关系，例如对于包含多个方面的句子，双向LSTM优于单向LSTM的性能，反之，对于仅包含一个方面的句子，反之亦然。 （C）2019 Elsevier B.V.保留所有权利。

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