论文标题
基于方面的情感分析通过教育水平的关注
Aspect-based Sentiment Analysis through EDU-level Attentions
论文作者
论文摘要
句子可能会对多个方面表达情感。当这些方面与不同的情感极性相关联时，模型的准确性通常会受到不利影响。我们观察到，这种艰难的句子中的多个方面主要通过多个子句或正式称为基本话语单位（EDUS）来表达，并且一个EDU倾向于表达一个单一方面对这一方面的单一情感。在本文中，我们建议在句子建模中考虑EDU边界，并在单词和EDU级别上进行关注。具体而言，我们通过单词级稀疏的注意力强调了edu中的情感单词。然后，在EDU级别，我们通过使用EDU级稀疏的注意力和正交正则化，强迫模型进入正确的EDU。三个基准数据集的实验表明，我们简单的教育模型的表现优于最先进的基准。因为可以以高精度将EDU自动分割，所以我们的模型可以直接应用于句子，而无需手动EDU边界注释。
A sentence may express sentiments on multiple aspects. When these aspects are associated with different sentiment polarities, a model's accuracy is often adversely affected. We observe that multiple aspects in such hard sentences are mostly expressed through multiple clauses, or formally known as elementary discourse units (EDUs), and one EDU tends to express a single aspect with unitary sentiment towards that aspect. In this paper, we propose to consider EDU boundaries in sentence modeling, with attentions at both word and EDU levels. Specifically, we highlight sentiment-bearing words in EDU through word-level sparse attention. Then at EDU level, we force the model to attend to the right EDU for the right aspect, by using EDU-level sparse attention and orthogonal regularization. Experiments on three benchmark datasets show that our simple EDU-Attention model outperforms state-of-the-art baselines. Because EDU can be automatically segmented with high accuracy, our model can be applied to sentences directly without the need of manual EDU boundary annotation.
