基于异核卷积双注意机制的立场检测研究

针对当前立场检测任务中目标短语在文本中隐式出现导致分类效果差的问题,提出一种基于异核卷积双注意机制(HCDAM)的立场检测模型.采用三段式策略,为提高目标短语和文本的特征表示能力,采用Bert预训练模型获得基于字符级的包含上下文的词向量表示;为提高隐式目标短语的抽取能力,采取异核卷积注意模式获取含不同位置和语义信息的卷...

Research on Stance Detection Based on Dual Attention Mechanism of Heteronuclear Convolution

Aiming at the problem that the target phrase in the current stance detection task appears implicitly in the text, which leads to the poor classification effect, a stance detection model based on Heteronuclear Convolution Double Attention Mechanism（HCDAM） is proposed. This method adopts a three-stage strategy. Firstly, in order to improve the feature representation ability of target phrase and text, the Bert pretraining model is used to obtain the word vector representation with context based on character level. Then, in order to improve the extraction ability of implicit target phrase, the heteronuclear convolution attention way is used to obtain the convolution features with different stance and semantic information. Finally, stance information feature is extracted by using explicit and implicit target phrases through re attention mechanism, and classified by softmax classifier. The experimental results based on NLPCC corpus show that, compared with the Bert-condition-CNN model, the average classification accuracy on the total dataset is improved by 0.108, and the classification accuracy on five topics is improved by 0.146, 0.046, 0.133, 0.047 and 0.056 respectively.