An Event Causality Identification Framework Using Ensemble Learning
Event causality identification is an upstream operation for many tasks, including knowledge graphs and intelligent question-and-answer systems. The latest models introduce external knowledge and then use deep learning for causality prediction. However, event causality recognition still faces problem...
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MDPI AG
2025-01-01
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| author | Xiaoyang Wang Wenjie Luo Xiudan Yang |
| author_facet | Xiaoyang Wang Wenjie Luo Xiudan Yang |
| author_sort | Xiaoyang Wang |
| collection | DOAJ |
| description | Event causality identification is an upstream operation for many tasks, including knowledge graphs and intelligent question-and-answer systems. The latest models introduce external knowledge and then use deep learning for causality prediction. However, event causality recognition still faces problems such as data imbalance and insufficient event content richness. Additionally, previous frameworks have utilized a single model, but these frequently produce unsatisfactory outcomes such as lower precision rates and lower recall rates. We propose the concept of ensemble learning, which combines multiple models to achieve frameworks that perform as well as or better than the latest models. This framework combines the advantages of Mamba, a temporal convolutional network, and graph computation to identify event causality more effectively and accurately. After comparing our framework to standard datasets, our F1-scores (measures of model accuracy) are essentially the same as those of the state-of-the-art (SOTA) methods on one dataset. |
| format | Article |
| id | doaj-art-262b0986cca040b2881de7b6949ced7d |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-262b0986cca040b2881de7b6949ced7d2025-01-24T13:35:13ZengMDPI AGInformation2078-24892025-01-011613210.3390/info16010032An Event Causality Identification Framework Using Ensemble LearningXiaoyang Wang0Wenjie Luo1Xiudan Yang2School of Cyber Security and Computer, Hebei University, Baoding 071002, ChinaSchool of Cyber Security and Computer, Hebei University, Baoding 071002, ChinaSchool of Management, Hebei University, Baoding 071002, ChinaEvent causality identification is an upstream operation for many tasks, including knowledge graphs and intelligent question-and-answer systems. The latest models introduce external knowledge and then use deep learning for causality prediction. However, event causality recognition still faces problems such as data imbalance and insufficient event content richness. Additionally, previous frameworks have utilized a single model, but these frequently produce unsatisfactory outcomes such as lower precision rates and lower recall rates. We propose the concept of ensemble learning, which combines multiple models to achieve frameworks that perform as well as or better than the latest models. This framework combines the advantages of Mamba, a temporal convolutional network, and graph computation to identify event causality more effectively and accurately. After comparing our framework to standard datasets, our F1-scores (measures of model accuracy) are essentially the same as those of the state-of-the-art (SOTA) methods on one dataset.https://www.mdpi.com/2078-2489/16/1/32event causality identifyensemble learningDistilBERTMambagraph neural network |
| spellingShingle | Xiaoyang Wang Wenjie Luo Xiudan Yang An Event Causality Identification Framework Using Ensemble Learning Information event causality identify ensemble learning DistilBERT Mamba graph neural network |
| title | An Event Causality Identification Framework Using Ensemble Learning |
| title_full | An Event Causality Identification Framework Using Ensemble Learning |
| title_fullStr | An Event Causality Identification Framework Using Ensemble Learning |
| title_full_unstemmed | An Event Causality Identification Framework Using Ensemble Learning |
| title_short | An Event Causality Identification Framework Using Ensemble Learning |
| title_sort | event causality identification framework using ensemble learning |
| topic | event causality identify ensemble learning DistilBERT Mamba graph neural network |
| url | https://www.mdpi.com/2078-2489/16/1/32 |
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