计算机应用与软件2018，Vol.35Issue(3)：266-274,9.DOI:10.3969/j.issn.1000-386x.2018.03.051
基于注意力的BiLSTM-CNN中文微博立场检测模型
ATTENTION BASED BILSTM-CNN CHINESE MICROBLOGGING POSITION DETECTION MODEL
白静 1李霏 1姬东鸿1
作者信息
- 1. 武汉大学计算机学院 湖北武汉430072
- 折叠
摘要
Abstract
For a large number of social network data,mining the information contained in the position has gradually become an important research direction.The Fifth Session of Natural Language Processing and Chinese Computing (Nlpcc2016)proposed the stance detection task for Chinese microblogging.In existing studies, researchers always use feature engineering such as adding emotional dictionaries and expert knowledge.But this approach requires a lot of manpower.Other researchers use the deep learning to detect the stance information.But they do not consider the fact that different words in sentences have different influences on position tendencies.Attention mechanisms are often used to optimize neural network models because they highlight the valuable features.This paper presented an attention-based Chinese microblogging position detection method for BiLSTM-CNN.Firstly, the text representation vector and the local convolution feature were obtained by Bi-directional long-short memory neural network(LSTM)and convolutional neural network(CNN)respectively.Then we added influence weight information to the local convolution features through Attention Mechanisms,and finally combined the two features to classify them.Experiments on Nlpcc corpus showed that the method of this article had achieved a good effect of position detection.The addition of attention mechanism could effectively improve the accuracy of position detection.关键词
立场检测/微博/神经网络/注意力机制Key words
Stance detection/Micro-blog/Neural networks/Attention mechanism分类
信息技术与安全科学引用本文复制引用
白静,李霏,姬东鸿..基于注意力的BiLSTM-CNN中文微博立场检测模型[J].计算机应用与软件,2018,35(3):266-274,9.
