Name
Abstract | ||
|---|---|---|
Deep learning has shown its effectiveness in many tasks such as text classification and computer vision. Most text classification tasks are concentrated in the use of convolution neural network and recurrent neural network to obtain text feature representation. In some researches, Attention mechanism is usually adopted to improve classification accuracy. According to the target of task 6 in NLP&CC2018, a hybrid deep learning model which combined BiGRU, CNN and Attention mechanism was proposed to improve text classification. The experimental results show that the F1-score of the proposed model successfully excels the task's baseline model. Besides, this hybrid Deep Learning model gets higher Precision, Recall and F1-score comparing with some other popular Deep Learning models, and the improvement of on F1-score is 5.4% than the single CNN model. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SKG.2018.00014 | 2018 14th International Conference on Semantics, Knowledge and Grids (SKG) |
Keywords | Field | DocType |
Semantics | Data mining,Convolutional neural network,Computer science,Recurrent neural network,Artificial intelligence,Deep learning,Recall,Semantics,Machine learning | Conference |
ISSN | ISBN | Citations |
2325-0623 | 978-1-7281-0441-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianglong Chen | 1 | 0 | 0.34 |
| OUYANG Chunping | 2 | 5 | 1.78 |
| Yongbin Liu | 3 | 58 | 11.05 |
| Lingyun Luo | 4 | 1 | 1.71 |
| Xiaohua Yang | 5 | 0 | 0.68 |