# Event Extraction by Associating Event Types and Argument Roles

arXiv: https://arxiv.org/abs/2108.10038 · HTML: https://mlsanity.com/paper/2108.10038/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [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)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [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)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs](https://mlsanity.com/paper/2101.08747/) (2021)
- [Joint Extraction of Events and Entities within a Document Context](https://mlsanity.com/paper/1609.03632/) (2016)
- [GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction](https://mlsanity.com/paper/2010.03009/) (2020)
- [Deep Learning for Community Detection: Progress, Challenges and Opportunities](https://mlsanity.com/paper/2005.08225/) (2020)
- [One for All: Neural Joint Modeling of Entities and Events](https://mlsanity.com/paper/1812.00195/) (2018)
- [Reading the Manual: Event Extraction as Definition Comprehension](https://mlsanity.com/paper/1912.01586/) (2019)
- [Open Domain Event Extraction Using Neural Latent Variable Models](https://mlsanity.com/paper/1906.06947/) (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)
- [UCD-CS at W-NUT 2020 Shared Task-3: A Text to Text Approach for COVID-19 Event Extraction on Social Media](https://mlsanity.com/paper/2009.10047/) (2020)
- [Open-domain Event Extraction and Embedding for Natural Gas Market Prediction](https://mlsanity.com/paper/1912.11334/) (2019)

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

- [Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network](https://mlsanity.com/paper/2412.15678/) (2024)
- [RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction](https://mlsanity.com/paper/2304.14770/) (2023)
- [Adaptive Differentially Private Structural Entropy Minimization for Unsupervised Social Event Detection](https://mlsanity.com/paper/2407.18274/) (2024)
- [RexUniNLU: Recursive Method with Explicit Schema Instructor for Universal NLU](https://mlsanity.com/paper/2409.05275/) (2024)
- [S2DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2412.15822/) (2024)

## Abstract (arXiv)

Event extraction (EE), which acquires structural event knowledge from texts, can be divided into two sub-tasks: event type classification and element extraction (namely identifying triggers and arguments under different role patterns). As different event types always own distinct extraction schemas (i.e., role patterns), previous work on EE usually follows an isolated learning paradigm, performing element extraction independently for different event types. It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles. This paper proposes a novel neural association framework for the EE task. Given a document, it first performs type classification via constructing a document-level graph to associate sentence nodes of different types, and adopting a graph attention network to learn sentence embeddings. Then, element extraction is achieved by building a universal schema of argument roles, with a parameter inheritance mechanism to enhance role preference for extracted elements. As such, our model takes into account type and role associations during EE, enabling implicit information sharing among them. Experimental results show that our approach consistently outperforms most state-of-the-art EE methods in both sub-tasks. Particularly, for types/roles with less training data, the performance is superior to the existing methods.