基于特征扩展卷积神经网络的案件微博观点句识别

通常的微博观点句识别主要根据微博评论本身是否带有观点来进行判断,而案件微博的观点句识别需要进一步考虑该评论是否讨论与特定案件相关的主题。针对这一任务,该文提出一种结合微博原文进行特征扩展的观点句识别模型。以卷积神经网络分类模型为基本框架,在嵌入层加入案件微博原文中的关键词向量,与对应评论词向量进行拼接;利用扩展的特征进行观点句识别。实验表明,该模型在根据案件微博爬取的两个数据集下准确率分别达到84.74%和82.09%,与现有的基准模型相比有较明显提升。

Feature-extended CNN Based Opinion Sentence Recognition from Case Related Microblog

In the case related microblogs, the opinion sentence recognition should consider whether the comment discusses the topic of a specific case. To address this issue, this paper proposes an opinion sentence recognition model that combines the microblogs content as the feature. Under the framework of CNN, the vector of keyword in the case related microblog is concatenated with the corresponding comment word vector at the input layer. Experiments show that the accuracy of the model on two datasets of case related microblogs reaches 84.74% and 82.09%, respectively, with a significant improvement compared with the existing benchmarks.