# Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding

arXiv: https://arxiv.org/abs/2110.07476 · HTML: https://mlsanity.com/paper/2110.07476/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [Zero-Shot Transfer Learning for Event Extraction](https://mlsanity.com/paper/1707.01066/) (2017)
- [Joint Extraction of Events and Entities within a Document Context](https://mlsanity.com/paper/1609.03632/) (2016)
- [CLEVE: Contrastive Pre-training for Event Extraction](https://mlsanity.com/paper/2105.14485/) (2021)
- [Unsupervised Label-aware Event Trigger and Argument Classification](https://mlsanity.com/paper/2012.15243/) (2020)
- [Reading the Manual: Event Extraction as Definition Comprehension](https://mlsanity.com/paper/1912.01586/) (2019)
- [Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph Convolution Neural Networks](https://mlsanity.com/paper/2010.14123/) (2020)
- [Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks](https://mlsanity.com/paper/2103.09330/) (2021)
- [Lexicon Infused Phrase Embeddings for Named Entity Resolution](https://mlsanity.com/paper/1404.5367/) (2014)
- [Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction](https://mlsanity.com/paper/2010.13391/) (2020)
- [Extending Event Detection to New Types with Learning from Keywords](https://mlsanity.com/paper/1910.11368/) (2019)

## Built on by (most cited first, 26 of 26)

- [Universal Information Extraction as Unified Semantic Matching](https://mlsanity.com/paper/2301.03282/) (2023)
- [TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction](https://mlsanity.com/paper/2311.09562/) (2023)
- [GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles](https://mlsanity.com/paper/2205.12505/) (2022)
- [Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection](https://mlsanity.com/paper/2204.07275/) (2022)
- [DICE: Data-Efficient Clinical Event Extraction with Generative Models](https://mlsanity.com/paper/2208.07989/) (2022)
- [AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model](https://mlsanity.com/paper/2305.16734/) (2023)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions](https://mlsanity.com/paper/2408.05357/) (2024)
- [Information Extraction in Low-Resource Scenarios: Survey and Perspective](https://mlsanity.com/paper/2202.08063/) (2022)
- [Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance](https://mlsanity.com/paper/2310.05991/) (2023)
- [The Art of Prompting: Event Detection based on Type Specific Prompts](https://mlsanity.com/paper/2204.07241/) (2022)
- [Global Constraints with Prompting for Zero-Shot Event Argument Classification](https://mlsanity.com/paper/2302.04459/) (2023)
- [RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction](https://mlsanity.com/paper/2304.14770/) (2023)
- [CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military News](https://mlsanity.com/paper/2404.12242/) (2024)
- [MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling](https://mlsanity.com/paper/2406.12420/) (2024)
- [Continual Event Extraction with Semantic Confusion Rectification](https://mlsanity.com/paper/2310.15470/) (2023)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Targeted Augmentation for Low-Resource Event Extraction](https://mlsanity.com/paper/2405.08729/) (2024)
- [Improve Event Extraction via Self-Training with Gradient Guidance](https://mlsanity.com/paper/2205.12490/) (2022)
- [CEO: Corpus-based Open-Domain Event Ontology Induction](https://mlsanity.com/paper/2305.13521/) (2023)
- [EDM3: Event Detection as Multi-task Text Generation](https://mlsanity.com/paper/2305.16357/) (2023)
- [A Survey of Document-Level Information Extraction](https://mlsanity.com/paper/2309.13249/) (2023)
- [MPL: Multiple Programming Languages with Large Language Models for Information Extraction](https://mlsanity.com/paper/2505.16107/) (2025)
- [MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection](https://mlsanity.com/paper/2305.09335/) (2023)
- [Nested Event Extraction upon Pivot Element Recogniton](https://mlsanity.com/paper/2309.12960/) (2023)
- [Beyond Exact Match: Semantically Reassessing Event Extraction by Large Language Models](https://mlsanity.com/paper/2410.09418/) (2024)

## Abstract (arXiv)

Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols. These approaches are usually limited to a set of pre-defined types. We propose a novel event extraction framework that uses event types and argument roles as natural language queries to extract candidate triggers and arguments from the input text. With the rich semantics in the queries, our framework benefits from the attention mechanisms to better capture the semantic correlation between the event types or argument roles and the input text. Furthermore, the query-and-extract formulation allows our approach to leverage all available event annotations from various ontologies as a unified model. Experiments on ACE and ERE demonstrate that our approach achieves state-of-the-art performance on each dataset and significantly outperforms existing methods on zero-shot event extraction.