Papers Event Relation Extraction

MAQInstruct: Instruction-based Unified Event Relation Extraction

Extracting event relations that deviate from known schemas has proven challenging for previous methods based on multi-class classification, MASK prediction, or prototype matching. Recent advancements in large language mo…

Event-Arguments Extraction Corpus and Modeling using BERT for Arabic

Event-argument extraction is a challenging task, particularly in Arabic due to sparse linguistic resources. To fill this gap, we introduce the \hadath corpus ($550$k tokens) as an extension of Wojood, enriched with event…

Are LLMs Good Annotators for Discourse-level Event Relation Extraction?

Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks. However, its effectiveness over discourse-level event relation extraction (ERE) tasks remains unexplored. I…

TacoERE: Cluster-aware Compression for Event Relation Extraction

Event relation extraction (ERE) is a critical and fundamental challenge for natural language processing. Existing work mainly focuses on directly modeling the entire document, which cannot effectively handle long-range d…

Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs

Event temporal relation (TempRel) is a primary subject of the event relation extraction task. However, the inherent ambiguity of TempRel increases the difficulty of the task. With the rise of prompt engineering, it is im…

GraphERE: Jointly Multiple Event-Event Relation Extraction via Graph-Enhanced Event Embeddings

Events describe the state changes of entities. In a document, multiple events are connected by various relations (e.g., Coreference, Temporal, Causal, and Subevent). Therefore, obtaining the connections between events th…

MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation

Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the an…

Improving Large Language Models in Event Relation Logical Prediction

Event relations are crucial for narrative understanding and reasoning. Governed by nuanced logic, event relation extraction (ERE) is a challenging task that demands thorough semantic understanding and rigorous logical re…

OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding

Event understanding aims at understanding the content and relationship of events within texts, which covers multiple complicated information extraction tasks: event detection, event argument extraction, and event relatio…

ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the in…

SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres

Event-centric structured prediction involves predicting structured outputs of events. In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complic…

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit ev…

Event-Event Relation Extraction using Probabilistic Box Embedding

To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task…

Selecting Optimal Context Sentences for Event-Event Relation Extraction

Understanding events entails recognizing the structural and temporal orders between event mentions to build event structures/ graphs for input documents. To achieve this goal, our work addresses the problems of subevent …

Event-Event Relation Extraction using Probabilistic Box Embedding

To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task…

From Discourse to Narrative: Knowledge Projection for Event Relation Extraction

Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. T…

Joint Constrained Learning for Event-Event Relation Extraction

Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. In this process, one can induce event complexes that organize multi-granular events wi…

Long-Distance Time-Event Relation Extraction