论文标题
位置意识衰减加权网络，用于基于方面的情感分析
A Position Aware Decay Weighted Network for Aspect based Sentiment Analysis
论文作者
论文摘要
基于方面的情感分析（ABSA）是识别给定其他文本段或方面的文本的情感极性的任务。在ABSA中，根据每个方面，文本可以具有多种情感。方面术语情感分析（ATSA）是ABSA的子任务，在给定句子中包含方面术语。为ATSA提出的大多数现有方法，通过不同的子网结合了方面信息，从而忽略了句子中的方面术语的优势。在本文中，我们提出了一个利用该方面的位置信息的模型。提出的模型基于位置引入了衰减机制。该衰减功能要求输入单词对ABSA的贡献。一个单词的贡献下降了，它与句子中的方面术语所定位。该性能是在Semeval 2014 Task 4的两个标准数据集上测量的。与最近的体系结构相比，该模型的有效性已被证明。
Aspect Based Sentiment Analysis (ABSA) is the task of identifying sentiment polarity of a text given another text segment or aspect. In ABSA, a text can have multiple sentiments depending upon each aspect. Aspect Term Sentiment Analysis (ATSA) is a subtask of ABSA, in which aspect terms are contained within the given sentence. Most of the existing approaches proposed for ATSA, incorporate aspect information through a different subnetwork thereby overlooking the advantage of aspect terms' presence within the sentence. In this paper, we propose a model that leverages the positional information of the aspect. The proposed model introduces a decay mechanism based on position. This decay function mandates the contribution of input words for ABSA. The contribution of a word declines as farther it is positioned from the aspect terms in the sentence. The performance is measured on two standard datasets from SemEval 2014 Task 4. In comparison with recent architectures, the effectiveness of the proposed model is demonstrated.
