Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports
This work addresses the need for efficient labeling of aviation accident reports to enhance safety analysis, representing an incremental improvement in domain-specific event extraction methods.
The paper tackled the problem of automatically extracting fine-level events from aviation accident reports by framing it as a hierarchical classification task, achieving improved fine-level prediction accuracy and better rare event identification through a novel hierarchical attention module and regularization based on event taxonomy.
A large volume of accident reports is recorded in the aviation domain, which greatly values improving aviation safety. To better use those reports, we need to understand the most important events or impact factors according to the accident reports. However, the increasing number of accident reports requires large efforts from domain experts to label those reports. In order to make the labeling process more efficient, many researchers have started developing algorithms to identify the underlying events from accident reports automatically. This article argues that we can identify the events more accurately by leveraging the event taxonomy. More specifically, we consider the problem a hierarchical classification task where we first identify the coarse-level information and then predict the fine-level information. We achieve this hierarchical classification process by incorporating a novel hierarchical attention module into BERT. To further utilize the information from event taxonomy, we regularize the proposed model according to the relationship and distribution among labels. The effectiveness of our framework is evaluated with the data collected by National Transportation Safety Board (NTSB). It has been shown that fine-level prediction accuracy is highly improved, and the regularization term can be beneficial to the rare event identification problem.