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Titlebook: Artificial Intelligence and Natural Language; 9th Conference, AINL Andrey Filchenkov,Janne Kauttonen,Lidia Pivovarova Conference proceeding

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樓主: choleric
41#
發(fā)表于 2025-3-28 18:01:13 | 只看該作者
42#
發(fā)表于 2025-3-28 22:39:08 | 只看該作者
43#
發(fā)表于 2025-3-29 02:10:43 | 只看該作者
44#
發(fā)表于 2025-3-29 03:22:45 | 只看該作者
https://doi.org/10.1007/978-3-662-06318-7plain that a generative model can improve accuracy and reduce the number of iteration steps for PageRank SSL. Moreover, we show that our framework outperforms the best graph-based SSL algorithms on four public citation graph data sets and improves the interpretability of classification results.
45#
發(fā)表于 2025-3-29 08:17:29 | 只看該作者
46#
發(fā)表于 2025-3-29 11:33:27 | 只看該作者
Advances of Transformer-Based Models for News Headline Generation,s on the RIA and Lenta datasets of Russian news. BertSumAbs increases ROUGE on average by 2.9 and 2.0 points respectively over previous best score achieved by Phrase-Based Attentional Transformer and CopyNet.
47#
發(fā)表于 2025-3-29 16:05:11 | 只看該作者
An Explanation Method for Black-Box Machine Learning Survival Models Using the Chebyshev Distance,termining important features and for explaining the black-box model prediction. Moreover, SurvLIME-Inf outperforms SurvLIME when the training set is very small. Numerical experiments with synthetic and real datasets demonstrate the SurvLIME-Inf efficiency.
48#
發(fā)表于 2025-3-29 22:12:53 | 只看該作者
Unsupervised Neural Aspect Extraction with Related Terms,demonstrate the effectiveness on the real-world dataset. We apply a special loss aimed to improve the quality of multi-aspect extraction. The experimental study demonstrates, what with this loss we increase the precision not only on this joint setting but also on aspect prediction only.
49#
發(fā)表于 2025-3-30 00:23:24 | 只看該作者
50#
發(fā)表于 2025-3-30 05:14:22 | 只看該作者
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