论文标题
令人尴尬的简单无监督的方面提取
Embarrassingly Simple Unsupervised Aspect Extraction
论文作者
论文摘要
我们提出了一种简单但有效的方法，用于情感分析中的方面识别。我们的无监督方法只需要嵌入单词和POS标记器，因此直接适用于新的域和语言。我们引入了对比度注意力（CAT），这是一种基于RBF内核的新型单头注意机制，它可以大大提高性能并使模型可解释。先前的工作依赖于句法特征和复杂的神经模型。我们表明，鉴于当前基准数据集在方面提取方面的简单性，不需要这种复杂的模型。重现本文报告的实验的代码，请访问https://github.com/clips/cat
We present a simple but effective method for aspect identification in sentiment analysis. Our unsupervised method only requires word embeddings and a POS tagger, and is therefore straightforward to apply to new domains and languages. We introduce Contrastive Attention (CAt), a novel single-head attention mechanism based on an RBF kernel, which gives a considerable boost in performance and makes the model interpretable. Previous work relied on syntactic features and complex neural models. We show that given the simplicity of current benchmark datasets for aspect extraction, such complex models are not needed. The code to reproduce the experiments reported in this paper is available at https://github.com/clips/cat
