# Tale of tails using rule augmented sequence labeling for event extraction

arXiv: https://arxiv.org/abs/1908.07018 · HTML: https://mlsanity.com/paper/1908.07018/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Event Argument Extraction using Causal Knowledge Structures](https://mlsanity.com/paper/2105.00477/) (2021)
- [ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation](https://mlsanity.com/paper/2106.10862/) (2021)
- [LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World](https://mlsanity.com/paper/2506.00980/) (2025)

## Abstract (arXiv)

The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data. Moreover, the task becomes complex for tail (rarely occurring) labels wherein extremely less data is available. In this paper, we present a new dataset (InDEE-2019) in the disaster domain for multiple Indic languages, collected from news websites. Using this dataset, we evaluate several rule-based mechanisms to augment deep learning based models. We formulate our problem of event extraction as a sequence labeling task and perform extensive experiments to study and understand the effectiveness of different approaches. We further show that tail labels can be easily incorporated by creating new rules without the requirement of large annotated data.