论文标题
通过明确利用方面对文档的情感组成，零射击方面级别的情感分类
Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition
论文作者
论文摘要
由于方面级别的情感标签是昂贵且富有劳动力的收购，因此提出了零击方面的情感分类，以学习适用于新域的分类器，而无需使用任何带注释的方面级别数据。相反，更容易访问具有评分的文档级别的情感数据。在这项工作中，我们仅通过使用文档级评论来实现零击方面的情感分类。我们的关键直觉是，文档的情感表示由该文档的所有方面的情感表示形式组成。基于此，我们提出了AF-DSC方法，以在评论中明确建模此类情感组成。 AF-DSC首先学习所有潜在方面的情感表示形式，然后将方面级别的情感汇总到文档级别的情绪中，以执行文档级别的情感分类。这样，我们将其作为文档级别的情感分类器的副产品获取方面级别的分类器。方面情感分类基准的实验结果表明，在文档级别的情感分类中，明确利用情感组成的有效性。我们的模型只有30k培训数据的表现优于先前的工作，利用数百万个数据。
As aspect-level sentiment labels are expensive and labor-intensive to acquire, zero-shot aspect-level sentiment classification is proposed to learn classifiers applicable to new domains without using any annotated aspect-level data. In contrast, document-level sentiment data with ratings are more easily accessible. In this work, we achieve zero-shot aspect-level sentiment classification by only using document-level reviews. Our key intuition is that the sentiment representation of a document is composed of the sentiment representations of all the aspects of that document. Based on this, we propose the AF-DSC method to explicitly model such sentiment composition in reviews. AF-DSC first learns sentiment representations for all potential aspects and then aggregates aspect-level sentiments into a document-level one to perform document-level sentiment classification. In this way, we obtain the aspect-level sentiment classifier as the by-product of the document-level sentiment classifier. Experimental results on aspect-level sentiment classification benchmarks demonstrate the effectiveness of explicit utilization of sentiment composition in document-level sentiment classification. Our model with only 30k training data outperforms previous work utilizing millions of data.
