# Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures

arXiv: https://arxiv.org/abs/2310.12074 · HTML: https://mlsanity.com/paper/2310.12074/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## 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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Coarse-to-Fine Pre-training for Named Entity Recognition](https://mlsanity.com/paper/2010.08210/) (2020)
- [Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.14565/) (2022)
- [A taxonomic system for failure cause analysis of open source AI incidents](https://mlsanity.com/paper/2211.07280/) (2022)
- [Transfer Learning for Information Extraction with Limited Data](https://mlsanity.com/paper/2003.03064/) (2020)
- [Jointly Learning Span Extraction and Sequence Labeling for Information Extraction from Business Documents](https://mlsanity.com/paper/2205.13434/) (2022)
- [Indexing AI Risks with Incidents, Issues, and Variants](https://mlsanity.com/paper/2211.10384/) (2022)
- [Emotion-Cause Pair Extraction as Question Answering](https://mlsanity.com/paper/2301.01982/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse](https://mlsanity.com/paper/2509.16722/) (2025)

## Abstract (arXiv)

This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (the IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).