# EventPlus: A Temporal Event Understanding Pipeline

arXiv: https://arxiv.org/abs/2101.04922 · HTML: https://mlsanity.com/paper/2101.04922/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://mlsanity.com/paper/1909.00277/) (2019)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [Content Planning for Neural Story Generation with Aristotelian Rescoring](https://mlsanity.com/paper/2009.09870/) (2020)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [Cross-media Structured Common Space for Multimedia Event Extraction](https://mlsanity.com/paper/2005.02472/) (2020)
- [Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction](https://mlsanity.com/paper/1909.05360/) (2019)
- [Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation](https://mlsanity.com/paper/1904.02357/) (2019)
- [GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction](https://mlsanity.com/paper/2010.03009/) (2020)
- [NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution](https://mlsanity.com/paper/1911.04211/) (2019)
- [Constructing Narrative Event Evolutionary Graph for Script Event Prediction](https://mlsanity.com/paper/1805.05081/) (2018)
- [An Improved Neural Baseline for Temporal Relation Extraction](https://mlsanity.com/paper/1909.00429/) (2019)
- [Deep Structured Neural Network for Event Temporal Relation Extraction](https://mlsanity.com/paper/1909.10094/) (2019)
- [CogCompTime: A Tool for Understanding Time in Natural Language Text](https://mlsanity.com/paper/1906.04940/) (2019)
- [Fine-Grained Temporal Relation Extraction](https://mlsanity.com/paper/1902.01390/) (2019)
- [Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction](https://mlsanity.com/paper/2009.07373/) (2020)
- [Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture](https://mlsanity.com/paper/1703.05851/) (2017)

## Built on by (most cited first, 17 of 17)

- [Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia](https://mlsanity.com/paper/2106.01601/) (2021)
- [NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge](https://mlsanity.com/paper/2206.07106/) (2022)
- [DICE: Data-Efficient Clinical Event Extraction with Generative Models](https://mlsanity.com/paper/2208.07989/) (2022)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts](https://mlsanity.com/paper/2205.01898/) (2022)
- [OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding](https://mlsanity.com/paper/2309.14258/) (2023)
- [Reasoning about Procedures with Natural Language Processing: A Tutorial](https://mlsanity.com/paper/2205.07455/) (2022)
- [STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models](https://mlsanity.com/paper/2305.15090/) (2023)
- [MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation](https://mlsanity.com/paper/2311.09105/) (2023)
- [Learning Action Conditions from Instructional Manuals for Instruction Understanding](https://mlsanity.com/paper/2205.12420/) (2022)
- [A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale](https://mlsanity.com/paper/2211.14358/) (2022)
- [Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs](https://mlsanity.com/paper/2403.15273/) (2024)
- [NECE: Narrative Event Chain Extraction Toolkit](https://mlsanity.com/paper/2208.08063/) (2022)
- [MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways](https://mlsanity.com/paper/2310.02529/) (2023)
- [RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor](https://mlsanity.com/paper/2312.03093/) (2023)
- [TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems](https://mlsanity.com/paper/2406.14825/) (2024)
- [Comparing Human and Large Language Model Interpretation of Implicit Information](https://mlsanity.com/paper/2604.17085/) (2026)

## Abstract (arXiv)

We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction. Event information, especially event temporal knowledge, is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. EventPlus as the first comprehensive temporal event understanding pipeline provides a convenient tool for users to quickly obtain annotations about events and their temporal information for any user-provided document. Furthermore, we show EventPlus can be easily adapted to other domains (e.g., biomedical domain). We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.