A Distributed Event Extraction Framework for Large-Scale Unstructured Text
Zhigang Kan, Haibo Mi, Sen Yang, Linbo Qiao, Dawei Feng, Dongsheng Li · 2020
Event extraction is an important subtask of information extraction. The goal of event extraction is to quickly extract events of a specified type from a large amount of textual information. Many excellent models and algorithms have been proposed since ACE released the event extraction task in 2005. Most of them are based on the dataset published by ACE and have contributed to the accuracy of event extraction to a certain extent. In practical applications, the processing object of the event extraction task is large-scale text data. However, as far as we know, there is currently no effective model for using multiple computers for event extraction. In this paper, we propose a model for event extraction based on inter-cloud computing technology. The experimental results prove that our method reduces the time consumption and also gets better accuracy than advanced models.