基于动态注意力GRU的特定目标情感分类

Aspect-based sentiment analysis based on dynamic attention GRU

特定目标情感分类是一个极具挑战的任务,其目的是确定上下文语境中某些实体所表达的情感.由于目标实体的情感依赖于实体本身以及对上下文的理解,在分类时应该同时考虑句子和目标实体,并且需要将目标实体与上下文语义进行充分的整合.因此,本文提出:(1)句子、目标实体联合编码方法;(2)基于动态注意力DAGRU (dynamic attention gated recurrent unit)的特定目标情感分析方法.联合编码是指同时对句子和目标实体进行语义编码,能为目标实体增加上下文语义;动态注意力机制能动态地改变模型对上下文单词的注意力以及目标实体的表示,从而更有效地获取上下文情感特征,使模型更准确地识别不同目标实体的情感类别.本文模型在SemEval2014的两个数据集Laptop, Restaurant上进行实验,实验结果表明,基于动态注意力DAGRU的模型相比基于标准注意力的模型结果有显著提高.

Aspect-level sentiment analysis is a fine-grained task that aims to identify the sentiment polarity(i.e.,negative,neutral,or positive)of a specific target opinion in its context.Since the sentiment polarity of a target depends on the target itself and the semantics of the context,the target and the sentence should be treated equally and modeled interactively.For aspect-level sentiment analysis,we propose(1)a method to encode the aspect and sentence simultaneously,and(2)a neural network based on a dynamic attention gated recurrent unit.The simultaneous encoding manner can generate the target representation,which contains more contextual clues.The dynamic attention mechanism can achieve the attention values of contextual words and further generate the target representation dynamically.Experimental results achieved on a SemEval 2014 dataset(Laptop and Restaurant)show that our approach achieves a significant improvement in the accuracy rates over the standard attention-based models.