Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our analysis demonstrates that our model can capture aspect-specific opinion spans.
翻译:先前的基于关注的模型强调使用侧面语义来帮助为分类提取意见特征。然而,这些作品要么无法从整体上捕捉各种观点,要么无法捕捉不同长度的观点。在本文中,我们通过汇总多个线性链通用报告格式,提出了一个清晰而有效的结构化关注模式。这样的设计使得模型能够提取特定方面的观点,然后通过利用提取的意见特征来评估情绪极性。四个数据集的实验结果显示了拟议模型的有效性,我们的分析表明,我们的模型可以捕捉特定方面的观点。