Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations

Research article (Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), 2018) · cited 27× · AI/ML

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Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations

Summary

Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations is a scholarly article^[1].

Key Facts

Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations's instance of is recorded as scholarly article^[2].

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Instance of scholarly article

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Authored by 3

K.Upendra Raju, Prafulla Kumar Choubey, Ruihong Huang

← Author of 3

K.Upendra Raju, Prafulla Kumar Choubey, Ruihong Huang

References

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Direct Wikidata claims

[2] ↑ Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations — instance of (P31): scholarly article. wikidata.org.

Class ancestry

[1] ↑ Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations is an instance of scholarly article (scholarly article) [P31, primary]. Wikidata. wikidata.org.

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APA

4ort.xyz Knowledge Graph. (2026). Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations. Retrieved May 24, 2026, from https://4ort.xyz/entity/identifying-the-most-dominant-event-in-a-news-article-by-mining-event-coreference-relations

MLA

“Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/identifying-the-most-dominant-event-in-a-news-article-by-mining-event-coreference-relations.

BibTeX

@misc{4ortxyz_identifying-the-most-dominant-event-in-a-news-article-by-mining-event-coreference-relations_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations}}, year = {2026}, url = {https://4ort.xyz/entity/identifying-the-most-dominant-event-in-a-news-article-by-mining-event-coreference-relations}, note = {Accessed: 2026-05-24}}

LLM prompt

According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations — https://4ort.xyz/entity/identifying-the-most-dominant-event-in-a-news-article-by-mining-event-coreference-relations (retrieved 2026-05-24)

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