Vsd168
发表于 2025-3-26 21:04:40
http://reply.papertrans.cn/23/2203/220231/220231_31.png
强壮
发表于 2025-3-27 01:53:20
http://reply.papertrans.cn/23/2203/220231/220231_32.png
使成波状
发表于 2025-3-27 09:17:15
Nachrichtenübertragung über Satellitenation (VTC) task. Meanwhile we propose a joint training framework for VCC task and VTC task based on adversarial perturbations strategy. In the final leaderboard, we achieved 3rd place in the competition. The source code has been at Github (.).
foliage
发表于 2025-3-27 12:39:44
https://doi.org/10.1007/978-3-7091-9534-5method, which improves the event detection ability of the model. At the same time, we use voting for model ensemble, so as to effectively utilize the advantages of multiple models. Our model achieves F1 score of 69.86% on the test set of CCKS2021 general fine-grained event detection task and ranks the third place in the competition.
名义上
发表于 2025-3-27 15:59:42
Zufall und lebendiges Geschehenbel entity typing. In our approach, a semi-supervised learning strategy is conducted to cope with the unlabeled data, and a multi-label loss is employed to recognize the multi-label entity. An F1-score of 0.85498 on the final testing data is achieved, which verifies the performance of our approach, and ranks the second place in the task.
implore
发表于 2025-3-27 21:27:14
Method Description for CCKS 2021 Task 3: A Classification Approach of Scholar Structured Informatioplied in academic searching. In this paper, a structured information extraction and match approach for structured scholar portrait from HTML web pages based on classification models is demonstrated in detail.
NIP
发表于 2025-3-27 23:07:58
A Joint Training Framework Based on Adversarial Perturbation for Video Semantic Tags Classificationation (VTC) task. Meanwhile we propose a joint training framework for VCC task and VTC task based on adversarial perturbations strategy. In the final leaderboard, we achieved 3rd place in the competition. The source code has been at Github (.).
metropolitan
发表于 2025-3-28 05:19:54
Data Augmentation Based on Pre-trained Language Model for Event Detection,method, which improves the event detection ability of the model. At the same time, we use voting for model ensemble, so as to effectively utilize the advantages of multiple models. Our model achieves F1 score of 69.86% on the test set of CCKS2021 general fine-grained event detection task and ranks the third place in the competition.
尖牙
发表于 2025-3-28 09:05:36
http://reply.papertrans.cn/23/2203/220231/220231_39.png
混沌
发表于 2025-3-28 10:59:07
A Biaffine Attention-Based Approach for Event Factor Extraction,several strategies, ensemble multi models to retrieve the final predictions. Eventually our approach performs on the competition data set well with an F1-score of 0.8033 and takes the first place on the leaderboard.