作呕
发表于 2025-3-28 14:37:19
Adversarial Domain Adaptation for Chinese Semantic Dependency Graph Parsingponent we proposed, the model can effectively improve the performance in the target domain. On the CCSD dataset, our model achieved state-of-the-art performance with significant improvement compared to the strong baseline model.
上流社会
发表于 2025-3-28 22:02:37
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dissolution
发表于 2025-3-29 01:43:09
Title-Aware Neural News Topic Predictionnews to learn unified news representations. In the title view, we learn title representations from words via a long-short term memory (LSTM) network, and use attention mechanism to select important words according to their contextual representations. In the body view, we propose to use a hierarchica
说明
发表于 2025-3-29 06:32:56
Lecture Notes in Computer Sciencet in BNC. They are . and . for the verb ., . for the verb ., and . for the verb .. (3) Some colligational patterns occur less frequently in CCE than those in BNC, such as the patterns . and . for the verb . and . for the verb ., and . for the verb .. (4) No new colligational patterns have been found
enmesh
发表于 2025-3-29 09:50:06
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慢慢流出
发表于 2025-3-29 11:39:17
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colony
发表于 2025-3-29 18:00:35
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FILTH
发表于 2025-3-29 22:26:35
Olivier Bournez,Enrico Formenti,Igor PotapovWe evaluate our model on two tasks: Answer Selection and Textual Entailment. Experimental results show the effectiveness of our model, which achieves the state-of-the-art performance on WikiQA dataset.
表主动
发表于 2025-3-30 00:28:45
Ilaria De Crescenzo,Salvatore La Torrenews to learn unified news representations. In the title view, we learn title representations from words via a long-short term memory (LSTM) network, and use attention mechanism to select important words according to their contextual representations. In the body view, we propose to use a hierarchica
苦恼
发表于 2025-3-30 06:42:01
https://doi.org/10.1007/978-3-030-32381-3artificial intelligence; classification; information extraction; language resources; machine translation