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Better Queries for Aspect-Category Sentiment Classification

机译：更好地查询方面类别情绪分类

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摘要

Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sentence is the key challenge to accurately recognize the sentiment polarity. Most previous models take both sentence and aspect category as input and query aspect category-related information based on the aspect category. However, these models represent the aspect category as a context-independent vector called aspect embedding, which may not be effective enough as a query. In this paper, we propose two contextualized aspect category representations, Contextualized Aspect Vector (CAV) and Contextualized Aspect Matrix (CAM). Specifically, we use the coarse aspect category-related information found by the aspect category detection task to generate CAV or CAM. Then the CAV or CAM as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. In experiments, we integrate the proposed CAV and CAM into several representative aspect embedding-based aspect-category sentiment classification models. Experimental results on the SemEval-2014 Restaurant Review dataset and the Multi-Aspect Multi-Sentiment dataset demonstrate the effectiveness of CAV and CAM.
机译：方面类别情绪分类（ACSC）旨在识别句子中提到的宽度类别的情感极性。因为一个句子经常提到一个以上的方面类别并表达了不同的情感极性，从句子中查找与之相关的相关信息是准确识别情感极性的关键挑战。最先前的模型将句子和方面类别作为输入和查询方面类别相关信息，基于方面类别。但是，这些模型将方面类别代表为名为Asport eMbedding的与上下文的向量，这可能与查询有足够的有效。在本文中，我们提出了两个上下文化的方面类别表示，上下文化的方向向量（CAV）和上下文化方面矩阵（CAM）。具体而言，我们使用由Aspect类别检测任务发现的粗略方面类别相关信息来生成CAV或凸轮。然后，CAV或CAM作为查询用于搜索由方面类别情绪分类模型的方面嵌入的方面与嵌入的细粒度与类别相关信息。在实验中，我们将建议的CAV和CAM集成到基于几个代表性方面的嵌入式方面情绪思想分类模型中。 Semeval-2014餐厅评论数据集的实验结果和多方面多种情绪数据集展示了CAV和CAM的有效性。

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