基于双嵌入卷积神经网络的涉案微博评价对象抽取

Aspect Extraction of Case Microblog Based on Double Embedded Convolutional Neural Network

涉案微博的评价对象抽取是一个特定领域的任务,其评价对象词表达多样且含义与通用领域不同,仅依赖于通用领域的词嵌入无法很好地表征这些评价对象词。为此,提出了一种综合利用领域词嵌入和通用词嵌入的涉案微博评价对象抽取方法。首先对涉案微博文本进行预训练,得到具有涉案领域特征的嵌入层,其次将微博评论分别输入两个嵌入层,得到不同领域对评价对象的表征结果并进行拼接操作,然后通过卷积层抽取出与案件相关的特征,最后利用分类器对序列进行标记,以提取涉案微博评价对象。实验结果表明,所提方法的F1值在#重庆公交车坠江案#和#奔驰女司机维权案#的两个数据集上分别达到了72.36%和71.02%,较现有的基准模型有所提升,验证了不同领域词嵌入对涉案微博评价对象抽取的影响。

Aspect extraction of the microblog involved in the case is a task in a specific domain.The expression of aspect words is diverse and the meaning is different from that of the general domain.Only relying on the word embedding in the general domain,these aspect words cannot be well represented.This paper proposes a method for extracting aspect words from microblogs by using both domain word embedding and generic word embedding.Firstly,all the microblogs involved in the case is pre-trained to obtain the embedding layer with the characteristics of the involved domain.Secondly,the microblog comments are input into two embedding layers to obtain the characterization results of the aspect words in different domains,and perform the splicing operation.Then,the features related to the case are extracted through the convolution layer.Finally,the classifier is used to label the sequence to extract aspect words involved in the case.The experimental results show that the F1 value of the proposed method reaches 72.36%and 71.02%respectively on the data sets of#Chongqing bus falling into the river#and#Mercedes Benz female driver rights protection#,which is better than the existing benchmark models,and verifies the influence of word embedding in different domains on the aspect extraction of the microblogs.