作者: CALL 時間: 2025-3-21 20:55 作者: 我就不公正 時間: 2025-3-22 00:24 作者: CURB 時間: 2025-3-22 08:15 作者: Pandemic 時間: 2025-3-22 10:01
Nachsorge nach Krebsoperationenraction for the Chinese electronic medical records. Two annotated data sets for the two subtasks were provided for participators. Our model on the test dataset achieves the strict F1-Measure of 0.7684 which ranked the first place.作者: 蛙鳴聲 時間: 2025-3-22 14:45 作者: 蛙鳴聲 時間: 2025-3-22 20:42 作者: 種族被根除 時間: 2025-3-23 01:11 作者: 罐里有戒指 時間: 2025-3-23 03:36 作者: 面包屑 時間: 2025-3-23 07:54 作者: Corral 時間: 2025-3-23 13:07 作者: BRAWL 時間: 2025-3-23 17:29 作者: 伙伴 時間: 2025-3-23 20:34
,Ged?chtnissrede auf Leopold kronecker,ore generalized information retrieval model and a more accurate semantic parsing model without manual involvement of templates. Our method achieves the averaged F1-score of 78.52% on the final test data, and ranks third in the KBQA task of CCKS 2021.作者: 永久 時間: 2025-3-23 23:23 作者: 一回合 時間: 2025-3-24 03:09
https://doi.org/10.1007/978-3-7091-9534-5e-grained event detection task, we propose an event detection scheme based on pre-trained model, combined with data augmentation and pseudo labelling 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 作者: 辯論 時間: 2025-3-24 07:11 作者: 芳香一點 時間: 2025-3-24 14:31
Hyperalimentation bei Krebspatienten, to interact with patients and provide clinical advice has attracted more and more attention. In the task of generative medical dialogue, the end-to-end method is often used to establish the model. However, traditional end-to-end models often generate deficient relevance to medical dialogue. Towards作者: Climate 時間: 2025-3-24 15:03
https://doi.org/10.1007/978-3-642-67520-1on Knowledge Graph and Semantic Computing (CCKS) 2021. For the NER task, we need to identify the entity boundaries and category labels of six types of entities from Chinese electronic medical records (EMR). And for the Event Extraction task, we need to recognizes a type of tumor event from Chinese E作者: Obverse 時間: 2025-3-24 21:40 作者: Bravura 時間: 2025-3-25 02:50 作者: Abbreviate 時間: 2025-3-25 06:26
Nachsorge und Krankheitsverlaufsanalysee labeled data sets. In contrast, information extraction directly oriented to web search results is more flexible, practical and challenging. The evaluation task of .-2021 “Aminer Scholar Profiling” requires accurate extraction of character attributes in the limited search range. A group of web info作者: Accommodation 時間: 2025-3-25 09:48
Nachsorge nach Krebsoperationenraction for the Chinese electronic medical records. Two annotated data sets for the two subtasks were provided for participators. Our model on the test dataset achieves the strict F1-Measure of 0.7684 which ranked the first place.作者: macular-edema 時間: 2025-3-25 11:51
J. Anderson,G. Goos,Claus Duhmeof event expressions to achieve the unity of data. Based on the Roberta pre-trained model, aiming at the problem of unbalanced distribution of difficult and easy cases in data, the effectiveness of various methods to enhance the generalization ability of the model is explored, including different da作者: semiskilled 時間: 2025-3-25 16:58
Wettbewerbsvorteil Nachtexpress,cts have fully proved its military value. Since knowledge graph is the information basis of intelligence, how to build a high-quality unmanned aerial vehicle knowledge graph is the focus of this paper. In this work, we propose an effective method to construct a knowledge graph from textual data. We 作者: BUST 時間: 2025-3-25 21:45 作者: 解決 時間: 2025-3-26 01:06
Grundlagen der digitalen Datenübertragungseveral 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.作者: 魅力 時間: 2025-3-26 04:38 作者: Obverse 時間: 2025-3-26 08:53 作者: ACTIN 時間: 2025-3-26 14:21
J. Anderson,G. Goos,Claus Duhmeta input methods, data enhancement, different loss functions, adversarial learning, contrastive learning. The best data input and model training methods are finally selected. On the CCKS2021 event co-reference resolution task for communication field, the f1 value of single model reaches 0.80 in test dataset 1 and 0.89 in test dataset 2.作者: 不知疲倦 時間: 2025-3-26 18:48
Conference proceedings 2022d in Guangzhou, China, in December 2021..The 17 competition papers went through a rigorious peer review and were accepted for publication.?CCKS 2021 technology evaluation track aims to provide researchers with platforms and resources for testing knowledge and semantic computing technologies, algorit作者: Vsd168 時間: 2025-3-26 21:04 作者: 強壯 時間: 2025-3-27 01:53 作者: 使成波狀 時間: 2025-3-27 09:17
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 (.).作者: foliage 時間: 2025-3-27 12:39
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.作者: 名義上 時間: 2025-3-27 15:59
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.作者: implore 時間: 2025-3-27 21:27
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.作者: NIP 時間: 2025-3-27 23:07
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 (.).作者: metropolitan 時間: 2025-3-28 05:19
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.作者: 尖牙 時間: 2025-3-28 09:05 作者: 混沌 時間: 2025-3-28 10:59
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.作者: 充滿裝飾 時間: 2025-3-28 16:14
A Multi-modal System for Video Semantic Understanding,. The VST model directly extracts semantic tags from text data with the combined model of ROBERTA and CRF. We implemented the system in the CCKS 2021 Task 14 and achieved an F1 score of 0.5054, ranking second among 187 teams.作者: 彎曲的人 時間: 2025-3-28 21:13 作者: RADE 時間: 2025-3-29 01:41
Strategies for Enhancing Generalization Ability of Communication Event Co-reference Resolution,ta input methods, data enhancement, different loss functions, adversarial learning, contrastive learning. The best data input and model training methods are finally selected. On the CCKS2021 event co-reference resolution task for communication field, the f1 value of single model reaches 0.80 in test dataset 1 and 0.89 in test dataset 2.作者: Diverticulitis 時間: 2025-3-29 03:49 作者: 光明正大 時間: 2025-3-29 07:50 作者: 鎮(zhèn)壓 時間: 2025-3-29 13:17
Wettbewerbsvorteil Nachtexpress,in-specific knowledge graph construction for military unmanned aerial vehicles) in CCKS 2021. There are two stages in this evaluation, and our approach achieves the second place in the first stage, i.e., knowledge graph quality evaluation and the third place in the second stage, i.e., knowledge graph usage evaluation.作者: 鍵琴 時間: 2025-3-29 17:56 作者: 啞劇 時間: 2025-3-29 22:08 作者: Sarcoma 時間: 2025-3-30 01:25 作者: 為現(xiàn)場 時間: 2025-3-30 04:40 作者: 武器 時間: 2025-3-30 09:16
Knowledge-Enhanced Retrieval: A Scheme for Question Answering,th predefined templates, as well as the . module to capture the best path. Extensive validations demonstrate the effectiveness of our KERQA framework, which achieved an F1 score of 78.78% on the final leaderboard of the CCKS 2021 KBQA contest.作者: 香料 時間: 2025-3-30 12:22 作者: 交響樂 時間: 2025-3-30 17:37
https://doi.org/10.1007/978-3-642-67520-1ng methods 3) Target instances constructed by similarity 4) Validation set retraining with a small learning rate 5) Medical word vector combined with Easy Data Augmentation (EDA) method for text data augmentation. Our innovative approach has improved by an average of 1.164% compared to the baseline.作者: Thyroid-Gland 時間: 2025-3-30 21:47
Hyperalimentation bei Krebspatienten,m the patient. The experimental results show that our framework achieves higher accuracy in disease diagnosis, which won the fourth place in the 2021 medical dialogue generation task containing Chinese.作者: Palpable 時間: 2025-3-31 01:58 作者: Debrief 時間: 2025-3-31 06:46 作者: Anonymous 時間: 2025-3-31 09:44
A Dual-Classifier Model for General Fine-Grained Event Detection Task,作者: Cytokines 時間: 2025-3-31 13:37
A Biaffine Attention-Based Approach for Event Factor Extraction,sed an approach with the biaffine attention mechanism to finish the task. The solution combines the state-of-the-art BERT-like base models and the biaffine attention mechanism to build a two-stage model, one stage for event trigger extraction and another for event role extraction. Besides, we apply 作者: 怎樣才咆哮 時間: 2025-3-31 19:49
Method Description for CCKS 2021 Task 3: A Classification Approach of Scholar Structured Informatiosearching task and recommendation system. Therefore, extracting, tagging and statistical analysis the precision facts of experts and scholar can be applied in academic searching. In this paper, a structured information extraction and match approach for structured scholar portrait from HTML web pages作者: 憤怒歷史 時間: 2025-3-31 23:29
A Joint Training Framework Based on Adversarial Perturbation for Video Semantic Tags Classification understanding framework into two related tasks, namely the multi-classes video cate classification (VCC) task and the multi-label video tag classification (VTC) task. Meanwhile we propose a joint training framework for VCC task and VTC task based on adversarial perturbations strategy. In the final
