{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:12:38Z","timestamp":1768522358065,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["62076249"],"award-info":[{"award-number":["62076249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["2020CXGC010701"],"award-info":[{"award-number":["2020CXGC010701"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Plan of Shandong Province","award":["62076249"],"award-info":[{"award-number":["62076249"]}]},{"name":"Key Research and Development Plan of Shandong Province","award":["2020CXGC010701"],"award-info":[{"award-number":["2020CXGC010701"]}]},{"name":"Fundamental Research Funds for the Universities of Liaoning Province","award":["62076249"],"award-info":[{"award-number":["62076249"]}]},{"name":"Fundamental Research Funds for the Universities of Liaoning Province","award":["2020CXGC010701"],"award-info":[{"award-number":["2020CXGC010701"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Event extraction is a complex and challenging task in the field of information extraction. It aims to identify event types, triggers, and argument information from the text. In recent years, overlapping event extraction has attracted the attention of researchers because of its higher challenge and practicability, and some work has carried out in-depth research on overlapping event extraction and achieved remarkable results. But these works (1) ignore the role of ontology knowledge in event extraction; (2) use the same semantic encoding for multi-stage models, lacking consideration for the independent characteristics of extraction tasks such as event types, triggers, and arguments; and (3) face issues in the training process of multi-stage models, such as error cascading and slow convergence. To address the above issues, we propose an ontology-guided and scheduled-sampling approach for overlapping event extraction, termed as OGSS. First, we design a symmetric matrix for event ontology knowledge representation and integrate it into the semantic encoding process, infusing ontology knowledge into event extraction. Second, for extraction targets such as event types, triggers, and arguments, we process the semantic encoding according to the characteristics of each extraction target, obtaining semantic representations tailored for each subtask. Finally, we view multi-stage predictions as sequential outputs of a joint model, using a scheduled sampling strategy between subtasks to effectively mitigate the cascading propagation of errors during training and accelerate model convergence. We conduct extensive experiments on the FewFc event extraction benchmark dataset. The results show that OGSS achieves significant improvements in overlapping event extraction tasks compared to previous methods.<\/jats:p>","DOI":"10.3390\/sym16091214","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T04:14:07Z","timestamp":1726460047000},"page":"1214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["OGSS: An Ontology-Guided and Scheduled-Sampling Approach for Overlapping Event Extraction"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0106-0170","authenticated-orcid":false,"given":"Jizhao","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Hualong","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China"}]},{"given":"Xinlong","family":"Pan","sequence":"additional","affiliation":[{"name":"Information Fusion Institute, Naval Aeronautical University, Yantai 264001, China"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"Neusoft Research, Northeastern University, Shenyang 110004, China"},{"name":"Department of Intelligent Human Resources and Social Security, Neusoft Inc., Shenyang 110179, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"133681","DOI":"10.1109\/ACCESS.2023.3332710","article-title":"An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems","volume":"11","author":"Jabbar","year":"2023","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"126680","DOI":"10.1016\/j.neucom.2023.126680","article-title":"Information retrieval algorithms and neural ranking models to detect previously fact-checked information","volume":"557","author":"Chakraborty","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s10506-023-09354-x","article-title":"Semantic matching based legal information retrieval system for COVID-19 pandemic","volume":"32","author":"Zhu","year":"2023","journal-title":"Artif. Intell. Law"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2341010","DOI":"10.1142\/S1793962323410106","article-title":"Intelligent question and answer analysis model of power ICT based on BI-LSTM-CRF","volume":"14","author":"Feifei","year":"2023","journal-title":"Int. J. Model. Simul. Sci. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, K., Ji, F., Lu, W., and Xiao, Y. (2022, January 22\u201328). Research on Text Generation of Medical Intelligent Question and Answer Based on Bi-LSTM and Neural Network Technology. Proceedings of the 2022 IEEE\/ACIS 22nd International Conference on Computer and Information Science (ICIS), Zhuhai, China.","DOI":"10.1109\/ICIS54925.2022.9882349"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cheng, Y. (2023, January 10\u201311). QA4C: An Intelligent Question and Answering System for the C Programming Language Based on Knowledge Graph. Proceedings of the 2023 10th International Conference on Dependable Systems and Their Applications (DSA), Tokyo, Japan.","DOI":"10.1109\/DSA59317.2023.00096"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/TPAMI.2022.3144993","article-title":"Reinforced, incremental and cross-lingual event detection from social messages","volume":"45","author":"Peng","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ren, J., Jiang, L., Peng, H., Liu, Z., Wu, J., and Philip, S.Y. (2022, January 11\u201315). Evidential temporal-aware graph-based social event detection via dempster-shafer theory. Proceedings of the 2022 IEEE International Conference on Web Services (ICWS), Barcelona, Spain.","DOI":"10.1109\/ICWS55610.2022.00055"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111225","DOI":"10.1016\/j.knosys.2023.111225","article-title":"Unsupervised social event detection via hybrid graph contrastive learning and reinforced incremental clustering","volume":"284","author":"Guo","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103525","DOI":"10.1016\/j.ipm.2023.103525","article-title":"GPR-OPT: A Practical Gaussian optimization criterion for implicit recommender systems","volume":"61","author":"Bai","year":"2024","journal-title":"Inf. Process. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"de Azambuja, R.X., Morais, A.J., and Filipe, V. (2023). X-wines: A wine dataset for recommender systems and machine learning. Big Data Cogn. Comput., 7.","DOI":"10.3390\/bdcc7010020"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/LCA.2023.3336841","article-title":"FPGA-Accelerated Data Preprocessing for Personalized Recommendation Systems","volume":"23","author":"Kim","year":"2023","journal-title":"IEEE Comput. Archit. Lett."},{"key":"ref_13","unstructured":"Chen, Y., Liu, S., He, S., Liu, K., and Zhao, J. (2016, January 15\u201316). Event extraction via bidirectional long short-term memory tensor neural networks. Proceedings of the Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data: 15th China National Conference, CCL 2016, and 4th International Symposium, NLP-NABD 2016, Yantai, China. Proceedings 4."},{"key":"ref_14","unstructured":"Li, Q., Ji, H., and Huang, L. (2013, January 4\u20139). Joint event extraction via structured prediction with global features. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Cho, K., and Grishman, R. (2016, January 12\u201317). Joint event extraction via recurrent neural networks. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1034"},{"key":"ref_16","unstructured":"Nguyen, T.M., and Nguyen, T.H. (February, January 27). One for all: Neural joint modeling of entities and events. Proceedings of the AAAI conference on artificial intelligence, Honolulu, HI USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, X., Luo, Z., and Huang, H. (2018). Jointly multiple events extraction via attention-based graph information aggregation. arXiv.","DOI":"10.18653\/v1\/D18-1156"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lu, Y., Lin, H., Xu, J., Han, X., Tang, J., Li, A., Sun, L., Liao, M., and Chen, S. (2021). Text2Event: Controllable sequence-to-structure generation for end-to-end event extraction. arXiv.","DOI":"10.18653\/v1\/2021.acl-long.217"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sheng, J., Guo, S., Yu, B., Li, Q., Hei, Y., Wang, L., Liu, T., and Xu, H. (2021). CasEE: A joint learning framework with cascade decoding for overlapping event extraction. arXiv.","DOI":"10.18653\/v1\/2021.findings-acl.14"},{"key":"ref_20","unstructured":"Yang, S., Feng, D., Qiao, L., Kan, Z., and Li, D. (August, January 28). Exploring pre-trained language models for event extraction and generation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_21","unstructured":"Xu, N., Xie, H., and Zhao, D. (November, January 30). A novel joint framework for multiple Chinese events extraction. Proceedings of the China National Conference on Chinese Computational Linguistics, Hainan, China."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, F., Peng, W., Chen, Y., Wang, Q., Pan, L., Lyu, Y., and Zhu, Y. (2020, January 16\u201320). Event extraction as multi-turn question answering. Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event.","DOI":"10.18653\/v1\/2020.findings-emnlp.73"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s13042-022-01760-y","article-title":"Label graph augmented soft cascade decoding model for overlapping event extraction","volume":"15","author":"Hei","year":"2024","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_24","unstructured":"Cao, H., Li, J., Su, F., Li, F., Fei, H., Wu, S., Li, B., Zhao, L., and Ji, D. (2022). OneEE: A one-stage framework for fast overlapping and nested event extraction. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ning, J., Yang, Z., Wang, Z., Sun, Y., and Lin, H. (2023, January 19\u201325). ODEE: A one-stage object detection framework for overlapping and nested event extraction. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Macao, China.","DOI":"10.24963\/ijcai.2023\/574"},{"key":"ref_26","unstructured":"Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N. (2015). Scheduled sampling for sequence prediction with recurrent neural networks. Adv. Neural Inf. Process. Syst., 28."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yu, B., Zhang, Z., Sheng, J., Liu, T., Wang, Y., Wang, Y., and Wang, B. (2021, January 19\u201323). Semi-open information extraction. Proceedings of the Web Conference 2021, Ljubljana, Slovenia.","DOI":"10.1145\/3442381.3450029"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wei, Z., Su, J., Wang, Y., Tian, Y., and Chang, Y. (2019). A novel cascade binary tagging framework for relational triple extraction. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jiang, Z., and Kong, F. (2023, January 26\u201329). PairEE: A Novel Pairing-Scoring Approach for Better Overlapping Event Extraction. Proceedings of the International Conference on Artificial Neural Networks, Crete, Greece.","DOI":"10.1007\/978-3-031-44201-8_11"},{"key":"ref_30","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Du, X., and Cardie, C. (2020). Document-level event role filler extraction using multi-granularity contextualized encoding. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.714"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., and Xu, B. (2017). Joint extraction of entities and relations based on a novel tagging scheme. arXiv.","DOI":"10.18653\/v1\/P17-1113"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/9\/1214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:57:21Z","timestamp":1760111841000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/9\/1214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,16]]},"references-count":32,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["sym16091214"],"URL":"https:\/\/doi.org\/10.3390\/sym16091214","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,16]]}}}