论文标题
通过锚定的正则化创建合奏，以进行无监督的方面提取
Ensemble Creation via Anchored Regularization for Unsupervised Aspect Extraction
论文作者
论文摘要
基于方面的情感分析是可以在文档 /句子上执行的最精细的情感分析形式。除了在精细的谷物上提供最多的见解外，它还带来了同样令人生畏的挑战。其中之一是标记数据的短缺。为了使价值从当今世界非常快速生成的文本数据中引入价值，基于方面的情感分析使我们能够生成洞察力，而无需花费时间或金钱来产生标签。从主题建模方法到最近的基于深度学习的方面提取模型，该领域已经有了很多发展。我们改进的模型之一是Abae，它将句子重建为存在的方面术语的线性组合，在这项研究中，我们探讨了如何使用来自另一个无监督模型的信息来正规化ABAE，从而提高性能更好。我们将其与基线规则的集合进行对比，并表明合奏方法比单个模型更好，基于正则化的合奏的性能比基于规则的组合更好。
Aspect Based Sentiment Analysis is the most granular form of sentiment analysis that can be performed on the documents / sentences. Besides delivering the most insights at a finer grain, it also poses equally daunting challenges. One of them being the shortage of labelled data. To bring in value right out of the box for the text data being generated at a very fast pace in today's world, unsupervised aspect-based sentiment analysis allows us to generate insights without investing time or money in generating labels. From topic modelling approaches to recent deep learning-based aspect extraction models, this domain has seen a lot of development. One of the models that we improve upon is ABAE that reconstructs the sentences as a linear combination of aspect terms present in it, In this research we explore how we can use information from another unsupervised model to regularize ABAE, leading to better performance. We contrast it with baseline rule based ensemble and show that the ensemble methods work better than the individual models and the regularization based ensemble performs better than the rule-based one.
