融合情感词交互注意力机制的属性抽取研究

Aspect Extraction via Interactive Attention with Sentiment Words

在现有的属性抽取研究中,注意力建模多采用全局或局部的自注意力机制,未能较好地利用句子本身所含有的情感词信息。然而,句子中所需抽取的属性大多存在若干与其具有强相关性的情感词。因此,利用情感词辅助注意力的建模可增强注意力的准确性。该文提出一种融合情感词的交互注意力机制,将文本中所有的情感词按序排列,并通过双向长短时记忆网络编码原始文本,利用全连接神经网络和高速网络编码排列的情感词;然后,利用情感词编码与原始文本编码建模交互注意力,从而使模型在情感词的辅助下精确地定位文中所包含的属性;最终,使用条件随机场进行属性标记。该文利用2014、2015语义评估属性级情感分析官方评测数据进行实验,验证了上述方法的有效性,该方法在三个基准数据集上F_(1)值分别提高了5.53、2.90和5.76个百分点。

Current aspect extraction methods adopt global or local self-attention mechanism without discriminating the sentiment words in the sentence.This paper proposes an aspect extraction method via interactive attention with sentiment words.We arrange all the sentiment word in the text in order,and apply Bi-directional Long-Short Term Memory network to encode sentence,and adopt fully connected neural network and High Way network to encode sentiment words.Then,we model the interactive attention with the sentence and sentiment words representations,which can precisely locate aspects with the help of sentiment words.Experimental results on three datasets from SemEval 2014-2015 show that our method can effectively improve the performance of aspect extraction by 5.53%、2.90%and 5.76%respectively.