论文标题
利用事件的特定和块跨度功能从推文中提取共证事件
Leveraging Event Specific and Chunk Span features to Extract COVID Events from tweets
论文作者
论文摘要
Twitter是灾难和大流行期间的重要信息来源，尤其是在Covid-19时期。在本文中，我们描述了WNUT 2020共享任务3的系统条目。该任务旨在自动从Twitter中提取各种Covid-19相关事件，例如最近感染该病毒的个体，患有症状的人被拒绝进行测试，并认为对感染的补救措施。该系统由用于插槽填充子任务和句子分类子任务的单独的多任务模型组成，同时利用相应事件的有用句子级信息。该系统使用Covid-twitter-Bert以及注意力加权候选插槽功能的集合来捕获有用的信息块。该系统以0.6598的F1在Leader-Board上排名第一，而无需使用任何合奏或其他数据集。代码和训练有素的模型可在此HTTPS URL上找到。
Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19. In this paper, we describe our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individuals who recently contracted the virus, someone with symptoms who were denied testing and believed remedies against the infection. The system consists of separate multi-task models for slot-filling subtasks and sentence-classification subtasks while leveraging the useful sentence-level information for the corresponding event. The system uses COVID-Twitter-Bert with attention-weighted pooling of candidate slot-chunk features to capture the useful information chunks. The system ranks 1st at the leader-board with F1 of 0.6598, without using any ensembles or additional datasets. The code and trained models are available at this https URL.
