Event Coreference Resolution
21 papers with code • 1 benchmarks • 3 datasets
We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution.
To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a large-scale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics.
Our simple system designed using minimal features achieved the micro-average F1 scores of 57. 72, 44. 27 and 42. 47 for event span detection, type identification and realis status classification tasks respectively.
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.
Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success.
This raises strong concerns on their generalizability -- a must-have for downstream applications where the magnitude of domains or event mentions is likely to exceed those found in a curated corpus.
We propose to leverage lexical paraphrases and high precision rules informed by news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles.
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution
Event coreference resolution is an important research problem with many applications.