# Financial Event Extraction Using Wikipedia-Based Weak Supervision

arXiv: https://arxiv.org/abs/1911.10783 · HTML: https://mlsanity.com/paper/1911.10783/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://mlsanity.com/paper/2206.01347/) (2022)
- [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://mlsanity.com/paper/2203.06482/) (2022)
- [Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading](https://mlsanity.com/paper/2105.12825/) (2021)
- [Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging](https://mlsanity.com/paper/2306.03723/) (2023)
- [Domain Regeneration: How well do LLMs match syntactic properties of text domains?](https://mlsanity.com/paper/2505.07784/) (2025)
- [An overview of event extraction and its applications](https://mlsanity.com/paper/2111.03212/) (2021)
- [An Annotated Commodity News Corpus for Event Extraction](https://mlsanity.com/paper/2105.08214/) (2021)
- [Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event Extraction](https://mlsanity.com/paper/2109.12781/) (2021)
- [Utilizing coarse-grained data in low-data settings for event extraction](https://mlsanity.com/paper/2205.05468/) (2022)
- [Sustainable Digitalization of Business with Multi-Agent RAG and LLM](https://mlsanity.com/paper/2502.15700/) (2025)
- [Writing Style Aware Document-level Event Extraction](https://mlsanity.com/paper/2201.03188/) (2022)

## Abstract (arXiv)

Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.