In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection alg...In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection algorithm based on a bidirectional long short-term memory(Bi-LSTM)network with position-weight is proposed.First,the corresponding position of the target in the input text is calculated with the ultimate position-weight vector.Next,the position information and output from the Bi-LSTM layer are fused by the position-weight fusion layer.Finally,the stances of different targets are predicted using the LSTM network and softmax classification.The multi-target stance detection corpus of the American election in 2016 is used to validate the proposed method.The results demonstrate that the Bi-LSTM network with position-weight achieves an advantage of 1.4%in macro average F1 value in the comparison of recent algorithms.展开更多
Stance detection aims to automatically determine whether the author is in favor of or against a given target.In principle,the sentiment information of a post highly influences the stance.In this study,we aim to levera...Stance detection aims to automatically determine whether the author is in favor of or against a given target.In principle,the sentiment information of a post highly influences the stance.In this study,we aim to leverage the sentiment information of a post to improve the performance of stance detection.However,conventional discrete models with sentimental features can cause error propagation.We thus propose a joint neural network model to predict the stance and sentiment of a post simultaneously,because the neural network model can learn both representation and interaction between the stance and sentiment collectively.Specifically, we first learn a deep shared representation between stance and sentiment information,and then use a neural stacking model to leverage sentimental information for the stance detection task.Empirical studies demonstrate the effectiveness of our proposed joint neural model.展开更多
立场检测研究旨在研究特定文本针对特定话题所表达的支持、中立或反对立场,在以往的中文文本立场分析研究方法中,未关注文本结构间的依赖关系,且评论文本所隐含的立场往往是隐晦和不敏感的。该文提出了基于双向Transformer的大规模预训...立场检测研究旨在研究特定文本针对特定话题所表达的支持、中立或反对立场,在以往的中文文本立场分析研究方法中,未关注文本结构间的依赖关系,且评论文本所隐含的立场往往是隐晦和不敏感的。该文提出了基于双向Transformer的大规模预训练语言模型BERT(Bidirectional Encoder Representation from Transformers)和长短时记忆(LSTM)以及卷积神经网络(CNN)相结合的立场分析方法来解决这个问题,同时,为解决BERT模型针对不同数据样本输入向量维度不一所导致的误差,提出了一种最优字个数维度判定算法对BERT模型输入进行分析。模型搭建上创新地采用并行输入输出的方法,充分利用了LSTM的全局特征提取和CNN的局部特征提取的优势,并且所用BERT模型更能对隐晦特征及不敏感特征进行提取,利用这一方法可以有效地判定不同目标对某一特定话题所表达的支持、中立或者反对立场。经过对比传统模型以及现有立场分析方法表明,所提模型拥有较好的性能,其F1值达到0.883。展开更多
基金Supported by the National Natural Science Foundation of China(No.61972040)the Science and Technology Projects of Beijing Municipal Education Commission(No.KM201711417011)the Premium Funding Project for Academic Human Resources Development in Beijing Union University(No.BPHR2020AZ03)。
文摘In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection algorithm based on a bidirectional long short-term memory(Bi-LSTM)network with position-weight is proposed.First,the corresponding position of the target in the input text is calculated with the ultimate position-weight vector.Next,the position information and output from the Bi-LSTM layer are fused by the position-weight fusion layer.Finally,the stances of different targets are predicted using the LSTM network and softmax classification.The multi-target stance detection corpus of the American election in 2016 is used to validate the proposed method.The results demonstrate that the Bi-LSTM network with position-weight achieves an advantage of 1.4%in macro average F1 value in the comparison of recent algorithms.
基金the National Natural Science Foundation of China (Grant Nos.61331011,61751206,61773276,61672366)Jiangsu Provincial Science and Technology Plan (BK20151222)Project of Natural Science Research of the Universities of Jiangsu Province.
文摘Stance detection aims to automatically determine whether the author is in favor of or against a given target.In principle,the sentiment information of a post highly influences the stance.In this study,we aim to leverage the sentiment information of a post to improve the performance of stance detection.However,conventional discrete models with sentimental features can cause error propagation.We thus propose a joint neural network model to predict the stance and sentiment of a post simultaneously,because the neural network model can learn both representation and interaction between the stance and sentiment collectively.Specifically, we first learn a deep shared representation between stance and sentiment information,and then use a neural stacking model to leverage sentimental information for the stance detection task.Empirical studies demonstrate the effectiveness of our proposed joint neural model.
文摘立场检测研究旨在研究特定文本针对特定话题所表达的支持、中立或反对立场,在以往的中文文本立场分析研究方法中,未关注文本结构间的依赖关系,且评论文本所隐含的立场往往是隐晦和不敏感的。该文提出了基于双向Transformer的大规模预训练语言模型BERT(Bidirectional Encoder Representation from Transformers)和长短时记忆(LSTM)以及卷积神经网络(CNN)相结合的立场分析方法来解决这个问题,同时,为解决BERT模型针对不同数据样本输入向量维度不一所导致的误差,提出了一种最优字个数维度判定算法对BERT模型输入进行分析。模型搭建上创新地采用并行输入输出的方法,充分利用了LSTM的全局特征提取和CNN的局部特征提取的优势,并且所用BERT模型更能对隐晦特征及不敏感特征进行提取,利用这一方法可以有效地判定不同目标对某一特定话题所表达的支持、中立或者反对立场。经过对比传统模型以及现有立场分析方法表明,所提模型拥有较好的性能,其F1值达到0.883。
