关键词: 中文微博, 立场检测, 注意力机制, 隐式特征, 深度学习

Abstract:

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.

Key words: Chinese microblog, stance detection, attention mechanism, implicit features, deep learning