摘要
事件抽取是从非结构化的文本中抽取用户感兴趣的事件信息,并以结构化的形式展现。当前社交媒体快速发展,互联网上突发事件信息数量也剧增。如何准确地从大量无结构事件信息中识别并抽取出突发事件信息,分析突发事件舆情趋势对社会安全极为重要。本文利用双向长短期记忆神经网络(BiLSTM)与图注意力机制(GAT)获取事件句子中的句法信息和获得事件论元之间的内在关联,进一步提升事件论元识别的准确率。通过在真实数据集中多次实验证明,本文中的方法在公开的数据集上进行验证,与以往的事件论元识别方法相比获得较大的性能提升。
Event extraction is to extract the event information that users are interested in from unstructured text and display it in a structured form. With the rapid development of social media, the amount of emergency event information on the Internet has also increased dramatically. How to accurately identify and extract emergent event information from a large amount of unstructured event information, and analyze the trend of public opinion on emergencies is extremely important to social security. In this paper, the Bi-directional Long Short-Term Memory (BiLSTM) and the graph attention network (GAT) are used to obtain the syntactic information in the event sentence and obtain the intrinsic correlation between the event arguments, so as to further improve the accuracy of the event argument recognition. Multiple experiments on real datasets prove that the method in this paper is validated on public datasets and achieves a large performance improvement compared with previous event argument extraction methods.
出处
《计算机科学与应用》
2022年第2期405-411,共7页
Computer Science and Application
