A distributed event extraction framework for large-scale unstructured text

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 real-world applications, the processing object of the event extraction task is large-scale text data. However, as far as we know, there is currently no adequate model for using multiple computers for event extraction. In this paper, we propose a framework for event extraction based on inter-cloud computing technology, which aims to extract events from huge-amount of unstructured text data in the wild. The experimental results demonstrate that our method could improve the throughput, reduce time consumption of the event extraction process, and further gets better accuracy than advanced models.