Titlebook: Chinese Computational Linguistics; 18th China National Maosong Sun,Xuanjing Huang,Yang Liu Conference proceedings 2019 Springer Nature Swi

作嘔

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.

上流社會(huì)

dissolution

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

說(shuō)明

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

慢慢流出

colony

FILTH

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.

表主動(dòng)

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

苦惱

https://doi.org/10.1007/978-3-030-32381-3artificial intelligence; classification; information extraction; language resources; machine translation