Key-Features Enhanced Financial Long Text Event Classification
[Objective/Significance]In order to address the issue of length limitations in long-text models,this study enhances the feature representation capability of the model by extracting event-related key sentences and keywords from long text.[Methods/Processes]The key-features enhanced model utilizes the TextRank algorithm to extract key event sentences and the TF-IDF algorithm to extract event keywords from the original text.These key features are used to enhance the long text,and further feature extraction is performed using BERT and Self-Attention models,followed by event classification.[Limitations]The model in this study was only tested on event classification in the financial domain.It is recommended to conduct further experiments and verify the effectiveness of the model in other domains as well.[Results/Conclusions]On the financial long news event classification dataset,the proposed model achieved an accuracy rate of 88.40%,outperforming other benchmark models by more than 2 percent,which demonstrates the superiority of the model.
Event ClassificationLong Text ClassificationKey FeaturesFeature EnhancementSelf-Attention Mechanism
NETL
NSTL
万方数据
