四溢 发表于 2025-3-28 16:01:26
http://reply.papertrans.cn/103/10217/1021658/1021658_41.pngINTER 发表于 2025-3-28 22:38:46
0302-9743 e on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30–September 1, 2024...The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions...The papers are organized in the following topical sections:.Part I: Natural language proc使显得不重要 发表于 2025-3-28 23:24:13
http://reply.papertrans.cn/103/10217/1021658/1021658_43.png臭了生气 发表于 2025-3-29 03:28:11
http://reply.papertrans.cn/103/10217/1021658/1021658_44.pngDefense 发表于 2025-3-29 09:34:21
A Boundary Feature Enhanced Span-Based Nested Named Entity Recognition Method, to improve the efficiency of BFSN2ER, we introduce a multi-task learning framework to achieve jointly models training. To validate the performance of BFSN2ER, experiments were conducted on three large datasets. Comparing with seven baselines, BFSN2ER achieved obviously better recall and F1-score,NAVEN 发表于 2025-3-29 11:33:52
http://reply.papertrans.cn/103/10217/1021658/1021658_46.pngLineage 发表于 2025-3-29 18:03:20
CeER: A Nested Name Entity Recognition Model Incorporating Gaze Features which reflect their importance in the reading cognitive process. Finally, we utilize the encoder improved by gaze feature learning and follow the question-answering architecture to identify all possible nested entities. We select three public eye-tracking datasets and two nested NER datasets, GENI共同时代 发表于 2025-3-29 22:10:51
Joint Semantic Relation Extraction for Multiple Entity Packetsng the fluctuations and regular semantics of entities. Finally, we aggregate the joint willingness among the entities in packets by combining the above two types of features, and thus extract the joint semantic relations effectively. Experimental results on various datasets illustrate that our methoreject 发表于 2025-3-30 00:11:44
http://reply.papertrans.cn/103/10217/1021658/1021658_49.pngMiddle-Ear 发表于 2025-3-30 05:13:00
Explicit Relation-Enhanced AMR for Document-Level Event Argument Extraction with Global-Local Attentles and trigger interaction. This module also improves the model’s efficiency in resource allocation and enables a more refined focus on relational data, which optimizes performance in event argument extraction. Empirical evidence from experiments conducted on WIKIEVENTS shows that our model, enhanc