Overall architecture of the FAR-AM model.

<p>The input text is first encoded by an LLM to generate short contexts (E1, E2, E3). Then, the context vectors (T1, T2, T3) were generated by the multi-layer Transformer module of BERT. Then, the output vector was convolved and pooled by TextCNN to extract local features and generate feature...

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Détails bibliographiques
Auteur principal:	Heng Peng (508975) (author)
Autres auteurs:	Kun Zhu (447919) (author)
Publié:	2025
Sujets:	Cell Biology Sociology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified specialized hybrid architectures large language model fragmented causal information fire accident reports enhancing public safety driven prevention strategies comprehensive ablation study content ), thucnews world firear corpus hybrid attention framework high semantic noise title ), agnews stage framework attention mechanism unique challenges subsequently processed specific tasks signal summaries results indicate layer self including roberta fused features first uses firear dataset final classification extreme length encoder layers developing data core architecture complex domain achieves 73
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Overall architecture of the FAR-AM model.

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