Proceedings of the Workshop on the Future of Event Detection (FuturED)

Joel Tetreault, Thien Huu Nguyen, Hemank Lamba, Amanda Hughes (Editors)

Anthology ID:: 2024.futured-1
Month:: November
Year:: 2024
Address:: Miami, Florida, USA
Venues:: FuturED | WS
SIG:
Publisher:: Association for Computational Linguistics
URL:: https://preview.aclanthology.org/Author-Pages-WenzhengZhang-ZhengyanShi-ShuYang/2024.futured-1/
DOI:: 10.18653/v1/2024.futured-1
Bib Export formats:: BibTeX
PDF:: https://preview.aclanthology.org/Author-Pages-WenzhengZhang-ZhengyanShi-ShuYang/2024.futured-1.pdf

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Proceedings of the Workshop on the Future of Event Detection (FuturED)
Joel Tetreault | Thien Huu Nguyen | Hemank Lamba | Amanda Hughes

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BERTrend: Neural Topic Modeling for Emerging Trends Detection
Allaa Boutaleb | Jerome Picault | Guillaume Grosjean

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An Incremental Clustering Baseline for Event Detection on Twitter
Marjolaine Ray | Qi Wang | Frédérique Mélanie-Becquet | Thierry Poibeau | Béatrice Mazoyer

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DEGREEˆ2: Efficient Extraction of Multiple Events Using Language Models
Philip Blair | Kfir Bar

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Reasoning and Tools for Human-Level Forecasting
Elvis Hsieh | Preston Fu | Jonathan Chen

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A Comprehensive Survey on Document-Level Information Extraction
Hanwen Zheng | Sijia Wang | Lifu Huang

Document-level information extraction (doc-IE) plays a pivotal role in the realm of natural language processing (NLP). This paper embarks on a comprehensive review and discussion of contemporary literature related to doc-IE. In addition, we conduct a thorough error analysis using state-of-the-art algorithms, shedding light on their limitations and remaining challenges for tackling the task of doc-IE. Our findings demonstrate that issues like entity coreference resolution and the lack of robust reasoning significantly hinder the effectiveness of document-level information extraction (doc-IE). Additionally, we uncover new challenges, including labeling noise and relation transitivity. The overarching objective of this survey paper is to provide valuable insights that can empower NLP researchers to further advance the performance of doc-IE.

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