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Titlebook: Natural Language Processing and Chinese Computing; 12th National CCF Co Fei Liu,Nan Duan,Yu Hong Conference proceedings 2023 The Editor(s)

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樓主: Maculate
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發(fā)表于 2025-3-23 13:25:19 | 只看該作者
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發(fā)表于 2025-3-23 16:23:30 | 只看該作者
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發(fā)表于 2025-3-23 19:52:23 | 只看該作者
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發(fā)表于 2025-3-23 23:44:40 | 只看該作者
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發(fā)表于 2025-3-24 04:28:59 | 只看該作者
Recurrent Transformers for?Long Document Understanding-range dependence. The ranking strategy is utilized to aggregate the local and global information for final prediction. Experiments on diverse tasks that require understanding long document demonstrate superior and robust performance of RTrans and our approach achieves a better balance between effectiveness and efficiency.
16#
發(fā)表于 2025-3-24 09:23:53 | 只看該作者
Chinese Event Causality Identification Based on?Retrieval Enhancement retrieval store of retrieved examples to serve as clues for reasoning about causality. Our experimental results on the only available Chinese causality dataset, show that our proposed method significantly improves the performance of Chinese event causality identification.
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發(fā)表于 2025-3-24 14:37:37 | 只看該作者
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發(fā)表于 2025-3-24 15:30:24 | 只看該作者
A Cross-lingual Sentiment Embedding Model with?Semantic and?Sentiment Joint Learningapanese, French and Spanish). Experimental results demonstrate UCSentiE’s stability in bilingual lexicon induction (BLI) and its superiority over unsupervised VecMap and supervised MUSE models in CLSA, with average F1 score improvements of about 6.53% and 2.23%, respectively. Visualization and clust
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發(fā)表于 2025-3-24 21:53:34 | 只看該作者
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發(fā)表于 2025-3-25 01:44:57 | 只看該作者
MACO: A Modality Adversarial and?Contrastive Framework for?Modality-Missing Multi-modal Knowledge Grto improve the performance of the generator. Experiments on public benchmarks with further explorations demonstrate that MACO could achieve state-of-the-art results and serve as a versatile framework to bolster various MMKGC models.
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