# An overview of event extraction and its applications

arXiv: https://arxiv.org/abs/2111.03212 · HTML: https://mlsanity.com/paper/2111.03212/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 111 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 23 of 23)

- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [Event Representations for Automated Story Generation with Deep Neural Nets](https://mlsanity.com/paper/1706.01331/) (2017)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction](https://mlsanity.com/paper/2010.03009/) (2020)
- [Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction](https://mlsanity.com/paper/2004.05438/) (2020)
- [Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks](https://mlsanity.com/paper/2104.00853/) (2021)
- [KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision](https://mlsanity.com/paper/2010.10833/) (2020)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [Open Domain Event Extraction Using Neural Latent Variable Models](https://mlsanity.com/paper/1906.06947/) (2019)
- [CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction](https://mlsanity.com/paper/2107.01583/) (2021)
- [Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation](https://mlsanity.com/paper/2005.11355/) (2020)
- [Financial Event Extraction Using Wikipedia-Based Weak Supervision](https://mlsanity.com/paper/1911.10783/) (2019)
- [Semi-Supervised Event Extraction with Paraphrase Clusters](https://mlsanity.com/paper/1808.08622/) (2018)
- [Open Event Extraction from Online Text using a Generative Adversarial Network](https://mlsanity.com/paper/1908.09246/) (2019)
- [Modality and Negation in Event Extraction](https://mlsanity.com/paper/2109.09393/) (2021)
- [Improving Pointwise Mutual Information (PMI) by Incorporating Significant Co-occurrence](https://mlsanity.com/paper/1307.0596/) (2013)
- [Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes](https://mlsanity.com/paper/1909.02126/) (2019)
- [ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19](https://mlsanity.com/paper/2105.01819/) (2021)
- [A Short Survey of Biomedical Relation Extraction Techniques](https://mlsanity.com/paper/1707.05850/) (2017)
- [Visualization Techniques to Enhance Automated Event Extraction](https://mlsanity.com/paper/2106.06588/) (2021)
- [Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction](https://mlsanity.com/paper/1709.01687/) (2017)

## Built on by (most cited first, 5 of 5)

- [Grounding Partially-Defined Events in Multimodal Data](https://mlsanity.com/paper/2410.05267/) (2024)
- [A Survey of Video Datasets for Grounded Event Understanding](https://mlsanity.com/paper/2406.09646/) (2024)
- [SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection](https://mlsanity.com/paper/2503.03303/) (2025)
- [Syntactic-GCN Bert based Chinese Event Extraction](https://mlsanity.com/paper/2112.09939/) (2021)
- [Schema-Constrained Document-Level Event Argument Extraction with Lightweight LLM Fine-Tuning](https://mlsanity.com/paper/2607.16808/) (2026)

## Abstract (arXiv)

With the rapid development of information technology, online platforms have produced enormous text resources. As a particular form of Information Extraction (IE), Event Extraction (EE) has gained increasing popularity due to its ability to automatically extract events from human language. However, there are limited literature surveys on event extraction. Existing review works either spend much effort describing the details of various approaches or focus on a particular field. This study provides a comprehensive overview of the state-of-the-art event extraction methods and their applications from text, including closed-domain and open-domain event extraction. A trait of this survey is that it provides an overview in moderate complexity, avoiding involving too many details of particular approaches. This study focuses on discussing the common characters, application fields, advantages, and disadvantages of representative works, ignoring the specificities of individual approaches. Finally, we summarize the common issues, current solutions, and future research directions. We hope this work could help researchers and practitioners obtain a quick overview of recent event extraction.