# Semantic Structure Enhanced Event Causality Identification

arXiv: https://arxiv.org/abs/2305.12792 · HTML: https://mlsanity.com/paper/2305.12792/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [Improving Question Answering by Commonsense-Based Pre-Training](https://mlsanity.com/paper/1809.03568/) (2018)
- [How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context](https://mlsanity.com/paper/2002.00652/) (2020)
- [Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement](https://mlsanity.com/paper/2106.01654/) (2021)
- [KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision](https://mlsanity.com/paper/2010.10833/) (2020)
- [Extracting Temporal and Causal Relations between Events](https://mlsanity.com/paper/1604.08120/) (2016)
- [LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification](https://mlsanity.com/paper/2106.01649/) (2021)
- [A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events](https://mlsanity.com/paper/1707.07343/) (2017)

## Built on by (most cited first, 15 of 15)

- [Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering](https://mlsanity.com/paper/2410.04752/) (2024)
- [Identifying while Learning for Document Event Causality Identification](https://mlsanity.com/paper/2405.20608/) (2024)
- [A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects](https://mlsanity.com/paper/2411.10371/) (2024)
- [ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction](https://mlsanity.com/paper/2309.12892/) (2023)
- [Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning](https://mlsanity.com/paper/2403.02893/) (2024)
- [In-context Contrastive Learning for Event Causality Identification](https://mlsanity.com/paper/2405.10512/) (2024)
- [Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering](https://mlsanity.com/paper/2403.11129/) (2024)
- [Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network](https://mlsanity.com/paper/2409.13621/) (2024)
- [MAQInstruct: Instruction-based Unified Event Relation Extraction](https://mlsanity.com/paper/2502.03954/) (2025)
- [AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction](https://mlsanity.com/paper/2406.10432/) (2024)
- [SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification](https://mlsanity.com/paper/2605.03701/) (2026)
- [Zero-Shot Event Causality Identification via Multi-source Evidence Fuzzy Aggregation with Large Language Models](https://mlsanity.com/paper/2506.05675/) (2025)
- [CaST: Causal Discovery via Spatio-Temporal Graphs in Disaster Tweets](https://mlsanity.com/paper/2602.02601/) (2026)
- [ConstructCIE: A Dataset for Extracting Causal Information from Construction Accident Narratives](https://mlsanity.com/paper/2608.06495/) (2026)
- [Resilient Concurrent Causal Discovery for Topological Event Sequences](https://mlsanity.com/paper/2608.21815/) (2026)

## Abstract (arXiv)

Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually expressed by implicit associations between events. Existing methods usually capture such associations by directly modeling the texts with pre-trained language models, which underestimate two kinds of semantic structures vital to the ECI task, namely, event-centric structure and event-associated structure. The former includes important semantic elements related to the events to describe them more precisely, while the latter contains semantic paths between two events to provide possible supports for ECI. In this paper, we study the implicit associations between events by modeling the above explicit semantic structures, and propose a Semantic Structure Integration model (SemSIn). It utilizes a GNN-based event aggregator to integrate the event-centric structure information, and employs an LSTM-based path aggregator to capture the event-associated structure information between two events. Experimental results on three widely used datasets show that SemSIn achieves significant improvements over baseline methods.