%0 Journal Article %1 journals/mta/0005LXPG24 %A Hu, Kai %A Li, Qing %A Xie, Jie %A Pu, Yingyan %A Guo, Ya %D 2024 %J Multim. Tools Appl. %K dblp %N 7 %P 18699-18720 %T Using pre-trained models and graph convolution networks to find the causal relations among events in the Chinese financial text data. %U http://dblp.uni-trier.de/db/journals/mta/mta83.html#0005LXPG24 %V 83

@article{journals/mta/0005LXPG24, added-at = {2024-02-28T00:00:00.000+0100}, author = {Hu, Kai and Li, Qing and Xie, Jie and Pu, Yingyan and Guo, Ya}, biburl = {https://www.bibsonomy.org/bibtex/274df705969a9072e655af6188f2f8dd1/dblp}, ee = {https://doi.org/10.1007/s11042-023-15496-6}, interhash = {cddb19f873f7ff7d6110307acc896a0e}, intrahash = {74df705969a9072e655af6188f2f8dd1}, journal = {Multim. Tools Appl.}, keywords = {dblp}, number = 7, pages = {18699-18720}, timestamp = {2024-04-08T15:16:04.000+0200}, title = {Using pre-trained models and graph convolution networks to find the causal relations among events in the Chinese financial text data.}, url = {http://dblp.uni-trier.de/db/journals/mta/mta83.html#0005LXPG24}, volume = 83, year = 2024 }