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Syntax-Directed Hybrid Attention Network for Aspect-Level Sentiment Analysis

IEEE Access(2018)SCI 3区SCI 4区

Chinese Acad Sci

Cited 23|Views52
Abstract
Aspect-level sentiment analysis is a fine-grained task in sentiment analysis that aims at detecting sentiment polarity towards a specific target in a sentence. Previous studies focus on using global attention mechanism that attends to all words in the context to model the interaction between target and sentence. However, global attention suffers from assigning high-attention score to irrelevant sentiment words in the cases where sentence contains noisy words or multiple targets. To address this problem, we propose a novel syntax-directed hybrid attention network (SHAN). In SHAN, a global attention is employed to capture coarse information about the target, and a syntax-directed local attention is used to take a look at words syntactically close to the target. An information gate is then utilized to synthesize the information from local and global attention results and adaptively generate a less-noisy and more sentiment-oriented representation. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of the proposed method.
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Key words
Aspect-level sentiment analysis,hybrid attention,syntactic information,gating mechanism
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要点】:本文提出了一种新的语法导向混合注意力网络(SHAN),用于方面级情感分析,通过全局注意力和语法导向的局部注意力结合信息门控机制,改善了全局注意力在处理噪声词或多个目标时的不足。

方法】:该方法结合了全局注意力和语法导向的局部注意力,并通过信息门控合成两种注意力的结果,生成更少噪声、更倾向情感的表达。

实验】:在SemEval 2014数据集上的实验结果表明,提出的SHAN方法在方面级情感分析任务上有效性得到了验证。