Event Relation Extraction
9 papers with code • 0 benchmarks • 1 datasets
To extract relations among events, such as event coreference, temporal, causal and subevent relations.
Benchmarks
These leaderboards are used to track progress in Event Relation Extraction
MAVEN-Arg
Most implemented papers
From Discourse to Narrative: Knowledge Projection for Event Relation Extraction
TangJiaLong/Knowledge-Projection-for-ERE
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ACL 2021
Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events.
Selecting Optimal Context Sentences for Event-Event Relation Extraction
hieumdt/SCS-EERE
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AAAI 2022
To achieve this goal, our work addresses the problems of subevent relation extraction (SRE) and temporal event relation extraction (TRE) that aim to predict subevent and temporal relations between two given event mentions/triggers in texts.
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude.
SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres
zjunlp/speech
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Event-centric structured prediction involves predicting structured outputs of events.
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 relation extraction.
Improving Large Language Models in Event Relation Logical Prediction
More in detail, we first investigate the deficiencies of LLMs in logical reasoning across different tasks.
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.
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.
