@inproceedings{ma-etal-2025-mining,
title = "Mining the Past with Dual Criteria: Integrating Three types of Historical Information for Context-aware Event Forecasting",
author = "Ma, Rong and
Wang, Lei and
Yang, Yating and
Ma, Bo and
Dong, Rui and
Yang, Fengyi and
Ahmat, Ahtamjan and
Lu, Kaiwen and
Wang, Xinyue",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.413/",
doi = "10.18653/v1/2025.emnlp-main.413",
pages = "8148--8163",
ISBN = "979-8-89176-332-6",
abstract = "Event forecasting requires modeling historical event data to predict future events, and achieving accurate predictions depends on effectively capturing the relevant historical information that aids forecasting. Most existing methods focus on entities and structural dependencies to capture historical clues but often overlook implicitly relevant information. This limitation arises from overlooking event semantics and deeper factual associations that are not explicitly connected in the graph structure but are nonetheless critical for accurate forecasting. To address this, we propose a dual-criteria constraint strategy that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information. Building on this strategy, our method, termed ITHI (Integrating Three types of Historical Information), combines sequential event information, periodically repeated event information, and relevant historical information to achieve context-aware event forecasting. We evaluated the proposed ITHI method on three public benchmark datasets, achieving state-of-the-art performance and significantly outperforming existing approaches. Additionally, we validated its effectiveness on two structured temporal knowledge graph forecasting dataset."
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<abstract>Event forecasting requires modeling historical event data to predict future events, and achieving accurate predictions depends on effectively capturing the relevant historical information that aids forecasting. Most existing methods focus on entities and structural dependencies to capture historical clues but often overlook implicitly relevant information. This limitation arises from overlooking event semantics and deeper factual associations that are not explicitly connected in the graph structure but are nonetheless critical for accurate forecasting. To address this, we propose a dual-criteria constraint strategy that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information. Building on this strategy, our method, termed ITHI (Integrating Three types of Historical Information), combines sequential event information, periodically repeated event information, and relevant historical information to achieve context-aware event forecasting. We evaluated the proposed ITHI method on three public benchmark datasets, achieving state-of-the-art performance and significantly outperforming existing approaches. Additionally, we validated its effectiveness on two structured temporal knowledge graph forecasting dataset.</abstract>
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%0 Conference Proceedings
%T Mining the Past with Dual Criteria: Integrating Three types of Historical Information for Context-aware Event Forecasting
%A Ma, Rong
%A Wang, Lei
%A Yang, Yating
%A Ma, Bo
%A Dong, Rui
%A Yang, Fengyi
%A Ahmat, Ahtamjan
%A Lu, Kaiwen
%A Wang, Xinyue
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ma-etal-2025-mining
%X Event forecasting requires modeling historical event data to predict future events, and achieving accurate predictions depends on effectively capturing the relevant historical information that aids forecasting. Most existing methods focus on entities and structural dependencies to capture historical clues but often overlook implicitly relevant information. This limitation arises from overlooking event semantics and deeper factual associations that are not explicitly connected in the graph structure but are nonetheless critical for accurate forecasting. To address this, we propose a dual-criteria constraint strategy that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information. Building on this strategy, our method, termed ITHI (Integrating Three types of Historical Information), combines sequential event information, periodically repeated event information, and relevant historical information to achieve context-aware event forecasting. We evaluated the proposed ITHI method on three public benchmark datasets, achieving state-of-the-art performance and significantly outperforming existing approaches. Additionally, we validated its effectiveness on two structured temporal knowledge graph forecasting dataset.
%R 10.18653/v1/2025.emnlp-main.413
%U https://aclanthology.org/2025.emnlp-main.413/
%U https://doi.org/10.18653/v1/2025.emnlp-main.413
%P 8148-8163
Markdown (Informal)
[Mining the Past with Dual Criteria: Integrating Three types of Historical Information for Context-aware Event Forecasting](https://aclanthology.org/2025.emnlp-main.413/) (Ma et al., EMNLP 2025)
ACL
- Rong Ma, Lei Wang, Yating Yang, Bo Ma, Rui Dong, Fengyi Yang, Ahtamjan Ahmat, Kaiwen Lu, and Xinyue Wang. 2025. Mining the Past with Dual Criteria: Integrating Three types of Historical Information for Context-aware Event Forecasting. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 8148–8163, Suzhou, China. Association for Computational Linguistics.