基于交互注意力图卷积网络的方面情感分类

基于方面的情感分类任务旨在识别句子中给定方面词的情感倾向性。以往的方法大多基于长短时记忆网络和注意力机制,这种做法在很大程度上仅依赖于建模句子中的方面词与其上下文的语义相关性,但忽略了句中的语法信息。针对这种缺陷,提出了一种交互注意力的图卷积网络,同时建模了句中单词的语义相关性和语法相关性。首先使用双向长短时记忆网络来学习句子的词序关系,捕捉句中上下文的语义信息;其次引入位置信息后,通过图卷积网络来学习句中的语法信息;然后通过一种掩码机制提取方面词;最后使用交互注意力机制,交互计算特定方面的上下文表示,并将其作为最后的分类特征。通过这种优势互补的设计,该模型可以很好地获得聚合了目标方面信息的上下文表示,并有助于情感分类。实验结果表明,该模型在多个数据集上都获得了优秀的效果。与未考虑语法信息的Bi-IAN模型相比,该模型在所有数据集上的结果均优于Bi-IAN模型,尤其在餐厅领域的REST14,REST15和REST16数据集上,该模型的F1值较Bi-IAN模型分别提高了4.17%,7.98%和8.03%;与同样考虑了语义信息和语法信息的ASGCN模型相比,该模型的F1值在除了LAP14数据集外的其他数据集上均优于ASGCN模型,尤其在餐厅领域的REST14,REST15和REST16数据集上,该模型的F1值较ASGCN模型分别提高了2.05%,1.66%和2.77%。

Interactive Attention Graph Convolutional Networks for Aspect-based Sentiment Classification

Aspect-based sentiment classification aims at identifying the sentiment polarity of the given aspect in a sentence.Most of the previous methods are based on long short-term memory network(LSTM)and attention mechanisms,which largely rely on the semantic correlation between aspects and contextual words in the modeled sentence,but ignore the syntactic information in the sentence.To tackle this problem,an interactive attention graph convolutional network(IAGCN)is proposed to model the semantic correlation and syntactic correlation of words in a sentence.Firstly,IAGCN starts with a bi-directional long short-term memory network(BiLSTM)to capture contextual semantic information regarding word orders.Then,the position information is introduced and put it into the graph convolutional network to learn the syntactic information.After that,aspect representation is obtained through mask mechanism.Finally,the interactive attention mechanism is used to interactively calculate and generate the aspect-specific contextual representation as the final classification feature.Through this complementary design,the model can obtain a good contextual representation that aggregates the aspect target information,and is helpful for sentiment classification.Experimental results show that the model achieve a good performance on multiple datasets.Compared with the Bi-IAN model without considering the syntax information,our model are superior to Bi-IAN model on all datasets,especially on the REST14,REST15 and REST16 datasets in the restaurant domain.Our model improves by 4.17%,7.98%and 8.03%on F1 scores respectively compare with the Bi-IAN model.Compared with the ASGCN model,which also takes semantic information and syntax information into account,the F1 scores of our model is better than that of the ASGCN model in all datasets except LAP14 dataset,especially on the REST14,REST15 and REST16 datasets in the restaurant domain.Compared with the ASGCN model,the F1 scores of our model is increased by 2.05%,1.66%and 2.77%respectively.