# Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports

arXiv: https://arxiv.org/abs/2403.17914 · HTML: https://mlsanity.com/paper/2403.17914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier](https://mlsanity.com/paper/2007.09898/) (2020)
- [TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories](https://mlsanity.com/paper/2004.13852/) (2020)
- [From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data](https://mlsanity.com/paper/2009.13794/) (2020)
- [Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy](https://mlsanity.com/paper/2006.15479/) (2020)
- [Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain](https://mlsanity.com/paper/2205.15952/) (2022)
- [Worldwide city transport typology prediction with sentence-BERT based supervised learning via Wikipedia](https://mlsanity.com/paper/2204.05193/) (2022)
- [Cause Identification from Aviation Safety Incident Reports via Weakly Supervised Semantic Lexicon Construction](https://mlsanity.com/paper/1401.4436/) (2014)

## Abstract (arXiv)

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.