# One for All: Neural Joint Modeling of Entities and Events

arXiv: https://arxiv.org/abs/1812.00195 · HTML: https://mlsanity.com/paper/1812.00195/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2018.

## Built on by (most cited first, 30 of 37)

- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [DeepStruct: Pretraining of Language Models for Structure Prediction](https://mlsanity.com/paper/2205.10475/) (2022)
- [Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks](https://mlsanity.com/paper/2103.09330/) (2021)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction](https://mlsanity.com/paper/2209.02693/) (2022)
- [CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction](https://mlsanity.com/paper/2107.01583/) (2021)
- [Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset](https://mlsanity.com/paper/2211.00869/) (2022)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction](https://mlsanity.com/paper/2301.02427/) (2023)
- [Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations](https://mlsanity.com/paper/2106.12384/) (2021)
- [FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction](https://mlsanity.com/paper/2202.08316/) (2022)
- [Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works](https://mlsanity.com/paper/2110.04525/) (2021)
- [MAILEX: Email Event and Argument Extraction](https://mlsanity.com/paper/2305.13469/) (2023)
- [Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs](https://mlsanity.com/paper/2406.01045/) (2024)
- [Event Extraction by Associating Event Types and Argument Roles](https://mlsanity.com/paper/2108.10038/) (2021)
- [DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction](https://mlsanity.com/paper/2310.10481/) (2023)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization](https://mlsanity.com/paper/2305.18926/) (2023)
- [A New Task and Dataset on Detecting Attacks on Human Rights Defenders](https://mlsanity.com/paper/2306.17695/) (2023)
- [Active Learning for Event Extraction with Memory-based Loss Prediction Model](https://mlsanity.com/paper/2112.03073/) (2021)
- [SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres](https://mlsanity.com/paper/2305.13617/) (2023)
- [A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction](https://mlsanity.com/paper/2305.15051/) (2023)
- [GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling](https://mlsanity.com/paper/2404.04763/) (2024)
- [Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field](https://mlsanity.com/paper/2212.08929/) (2022)
- [Writing Style Aware Document-level Event Extraction](https://mlsanity.com/paper/2201.03188/) (2022)
- [Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach](https://mlsanity.com/paper/2205.00387/) (2022)
- [Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus](https://mlsanity.com/paper/2206.07472/) (2022)
- [PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture](https://mlsanity.com/paper/2211.12157/) (2022)

## Abstract (arXiv)

The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.