# OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction

arXiv: https://arxiv.org/abs/2209.02693 · HTML: https://mlsanity.com/paper/2209.02693/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 60 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://mlsanity.com/paper/2010.13415/) (2020)
- [Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus](https://mlsanity.com/paper/2004.06295/) (2020)
- [Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy](https://mlsanity.com/paper/1909.04273/) (2019)
- [One for All: Neural Joint Modeling of Entities and Events](https://mlsanity.com/paper/1812.00195/) (2018)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Dependency-Guided LSTM-CRF for Named Entity Recognition](https://mlsanity.com/paper/1909.10148/) (2019)
- [End-to-end Semantic Role Labeling with Neural Transition-based Model](https://mlsanity.com/paper/2101.00394/) (2021)
- [Discontinuous Named Entity Recognition as Maximal Clique Discovery](https://mlsanity.com/paper/2106.00218/) (2021)
- [Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2106.03315/) (2021)
- [Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities](https://mlsanity.com/paper/1909.00930/) (2019)
- [CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction](https://mlsanity.com/paper/2107.01583/) (2021)
- [Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction](https://mlsanity.com/paper/2010.04640/) (2020)
- [Personalized Hashtag Recommendation for Micro-videos](https://mlsanity.com/paper/1908.09987/) (2019)

## Built on by (most cited first, 18 of 18)

- [Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination](https://mlsanity.com/paper/2305.12256/) (2023)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [Entity-centered Cross-document Relation Extraction](https://mlsanity.com/paper/2210.16541/) (2022)
- [Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling](https://mlsanity.com/paper/2305.11719/) (2023)
- [Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction](https://mlsanity.com/paper/2305.12258/) (2023)
- [CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military News](https://mlsanity.com/paper/2404.12242/) (2024)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [ECQED: Emotion-Cause Quadruple Extraction in Dialogs](https://mlsanity.com/paper/2306.03969/) (2023)
- [TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2306.03974/) (2023)
- [Revisiting Conversation Discourse for Dialogue Disentanglement](https://mlsanity.com/paper/2306.03975/) (2023)
- [JSEEGraph: Joint Structured Event Extraction as Graph Parsing](https://mlsanity.com/paper/2306.14633/) (2023)
- [DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs](https://mlsanity.com/paper/2308.04498/) (2023)
- [ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models](https://mlsanity.com/paper/2403.05132/) (2024)
- [TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems](https://mlsanity.com/paper/2406.14825/) (2024)
- [Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction](https://mlsanity.com/paper/2504.21372/) (2025)
- [Nested Event Extraction upon Pivot Element Recogniton](https://mlsanity.com/paper/2309.12960/) (2023)
- [Syntactic Fusion: Enhancing Aspect-Level Sentiment Analysis Through Multi-Tree Graph Integration](https://mlsanity.com/paper/2312.03738/) (2023)
- [Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2312.03739/) (2023)

## Abstract (arXiv)

Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. Most prior work focuses on extracting flat events while neglecting overlapped or nested ones. A few models for overlapped and nested EE includes several successive stages to extract event triggers and arguments,which suffer from error propagation. Therefore, we design a simple yet effective tagging scheme and model to formulate EE as word-word relation recognition, called OneEE. The relations between trigger or argument words are simultaneously recognized in one stage with parallel grid tagging, thus yielding a very fast event extraction speed. The model is equipped with an adaptive event fusion module to generate event-aware representations and a distance-aware predictor to integrate relative distance information for word-word relation recognition, which are empirically demonstrated to be effective mechanisms. Experiments on 3 overlapped and nested EE benchmarks, namely FewFC, Genia11, and Genia13, show that OneEE achieves the state-of-the-art (SOTA) results. Moreover, the inference speed of OneEE is faster than those of baselines in the same condition, and can be further substantially improved since it supports parallel inference.