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Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

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發(fā)表于 2025-3-28 16:01:26 | 只看該作者
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發(fā)表于 2025-3-28 22:38:46 | 只看該作者
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
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發(fā)表于 2025-3-28 23:24:13 | 只看該作者
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發(fā)表于 2025-3-29 03:28:11 | 只看該作者
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發(fā)表于 2025-3-29 09:34:21 | 只看該作者
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,
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發(fā)表于 2025-3-29 11:33:52 | 只看該作者
47#
發(fā)表于 2025-3-29 18:03:20 | 只看該作者
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
48#
發(fā)表于 2025-3-29 22:10:51 | 只看該作者
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 metho
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發(fā)表于 2025-3-30 00:11:44 | 只看該作者
50#
發(fā)表于 2025-3-30 05:13:00 | 只看該作者
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
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