论文标题
基于食物危害阿拉伯文本中深度学习的事件提取
Event Extraction Based on Deep Learning in Food Hazard Arabic Texts
论文作者
论文摘要
社交媒体网站已将数字设备传播给公众，使信息共享更加容易，更快。交换文本数据是社交媒体用户中最受欢迎的通信。它已成为治疗的必要性。另一方面，事件提取表明对社交媒体帖子流中的事件有了解。事件提取有助于在自然灾害中采取更快的纠正措施，并可以挽救生命。该任务的主要目的是开发特定模型，以检测和提取数字文本中确定的事件（事件）。我们在这里提出了一个基于深度复发网络的模型，以从社交媒体提要中提取事件。
Social Media websites have disseminated digital devices to the public, making information sharing easier and faster. Exchanging textual data is the most popular communication among social media users. It has become a necessity for treatment. Event extraction on the other hand indicates an understanding of events across social media posts streams. Event extraction helps to take faster corrective action in natural disasters, and may save lives. The main objective of the task is to develop specific model to detect and extract the events (incidents) identified in the digital text. We proposed here a model based on deep recurrent networks to extract the events from social media feeds.
