## 6 Conclusions  
The proposed model GPRL, based on relation filtering and multi-task learning for entity relation extraction, was introduced in this research. The model utilizes a global pointer network to transform the extraction of triples into quintuples and converts the extraction of entity heads and tails under specific relations into a form similar to scaled dot-product attention. GPRL can identify overlapping triples and nested entities by employing multiple specific relation matrices. Traditional pointer networks typically suffer from the inability to consider entity positions globally, leading to inconsistencies between training and prediction. On the other hand, the global pointer network used in GPRL enhances model performance by enabling holistic discrimination of entity positions, which ensures consistency between training and prediction objectives. In addition, the implementation of relation filtering within the global pointer network considerably reduces the necessity for repetitive relation judgments. This approach enhances the model's accuracy and efficiency by eliminating the requirement for manually annotating entities for each predefined relation in the dataset, leading to more accurate triple extraction. Further, the use of a multi-task learning strategy successfully handles inter-task imbalances, which is particularly beneficial in situations with limited training data. This strategy also mitigates the challenge of overfitting, resulting in significant improvement in the model's performance. In comparison experiments conducted on the publicly available Chinese datasets CMEIE and DuIE2.0, as well as complex and ablation experiments, GPRL consistently outperformed current baseline models and proved state-of-the-art performance in Chinese datasets for entity relation extraction.  
Future research will focus on advancing entity relation extraction by developing innovative approaches, refining model architectures, and optimizing training strategies to improve generalizability and performance. We aim to enhance practical applications and build knowledge graphs and intelligent Q&A systems that can understand user queries, retrieve accurate answers, and engage in complex reasoning. This will elevate user experience and expand AI applications in sectors like customer service, education, healthcare, and environmental resources.  
Conceptualization, B.L.; methodology, W.C.; software, W.C.; validation, Y.Z. and W.C.; formal analysis, J.T.; investigation, L.H.; resources, L.H.; data curation, Y.Z. and M.C.; writing--original draft preparation, J.T.; writing--review and editing, B.L.; visualization, J.T.; supervision, L.H.; project administration, D.T.; funding acquisition, D.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Major Science and Technology Projects of Sichuan Province, grant number 2022 ZDZX0001, The Science and Technology Support Project of Sichuan Province, grant number 2023 YS0366 and 2023 YFG0020, and Natural Science Foundation of Sichuan Province, grant number 2024 NSFSC0792.  
The CMeIE dataset that supports the findings of this study is openly available in [TIANCHI Data Platform] at [[https://tianchi.aliyun.com/dataset/95414](https://tianchi.aliyun.com/dataset/95414)]([https://tianchi.aliyun.com/dataset/95414](https://tianchi.aliyun.com/dataset/95414)) (22 March 2021). The DuIE2.0 dataset that supports the findings of this study is openly available in [AI Studio Data Platform] at [[https://aistudio.baidu.com/datasetdetail/180082](https://aistudio.baidu.com/datasetdetail/180082)]([https://aistudio.baidu.com/datasetdetail/180082](https://aistudio.baidu.com/datasetdetail/180082)) (28 November 2022).  
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.