基于注意力双层BiReGU模型的方面术语提取方法

doi:10.3778/j.issn.1002-8331.2007-0038

摘要/Abstract

摘要：

方面术语提取是方面级情感分析中的一项重要任务，目的是从在线产品评论中提取关键的方面术语。针对方面术语提取问题，提出基于注意力机制的双层BiReGU模型。该模型在传统BiLSTM模型的基础上，引入双嵌入机制和ReGU（Residual Gated Unit）作为辅助，以提高特征提取的能力。使用BiReGU学习文本特征表示，更好地捕捉词语间的长期依赖关系；在第一层BiReGU之后引入注意力机制，为文本中每个词语赋予不同的权重，得到融合特征后新的知识表示，再输入到第二层BiReGU中学习更加全局的文本特征表示，最后完成提取方面术语的任务。分别在SemEval 2014的Restaurant数据集和Laptop数据集做了相关的对比实验，实验结果证明了所提出方法的有效性。

关键词: 注意力机制, 方面术语提取, BiReGU, 方面级情感分析, 深度学习

Abstract:

In aspect-based sentiment analysis, aspect term extraction is an important task, which aims to extract key aspect term from online product reviews. To solve the problem of aspect term extraction, attention-based double BiReGU model is proposed. Based on the traditional BiLSTM, this model introduces double embedding mechanism and ReGU（Residual Gated Unit） as auxiliary to improve the ability of feature extraction. Firstly, BiReGU is used to learn textual feature representation to capture the long-term dependence between words better. Secondly, the attention mechanism is introduced after the first layer BiReGU, and different weights are assigned to each word to obtain the new knowledge representation after the fusion of feature, which is input to the second layer BiReGU to learn more global representation of text feature. Finally, the task of extracting aspect term is realized. The experimental results on Laptop and Restaurant datasets in the classic SemEval 2014 demonstrate the effectiveness of the proposed model.

Key words: attention mechanism, aspect term extraction, BiReGU, aspect-based sentiment analysis, deep learning

赵丽华，王春立，初钰凤. 基于注意力双层BiReGU模型的方面术语提取方法[J]. 计算机工程与应用, 2021, 57(22): 160-165.

ZHAO Lihua, WANG Chunli, CHU Yufeng. Attention-Based Double BiReGU Model for Aspect Term Extraction[J]. Computer Engineering and Applications, 2021, 57(22): 160-165.

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