Name
Title | ||
|---|---|---|
Integrating Bi-Dynamic Routing Capsule Network with Label-Constraint for Text classification |
Abstract | ||
|---|---|---|
Neural-based text classification methods have attracted increasing attention in recent years. Unlike the standard text classification methods, neural-based text classification methods perform the representation operation and end-to-end learning on the text data. Many useful insights can be derived from neural based text classifiers as demonstrated by an ever-growing body of work focused on text mining. However, in the real-world, text can be both complex and noisy which can pose a problem for effective text classification. An effective way to deal with this issue is to incorporate self-attention and capsule networks into text mining solutions. In this paper, we propose a Bi-dynamic routing Capsule Network with Label-constraint (BCNL) model for text classification, which moves beyond the limitations of previous methods by automatically learning the task-relevant and label-relevant words of text. Specifically, we use a Bi-LSTM and self-attention with position encoder network to learn text embeddings. Meanwhile, we propose a bi-dynamic routing capsule network with label-constraint to adjust the category distribute of text capsules. Through extensive experiments on four datasets, we observe that our method outperforms state-of-the-art baseline methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICBK50248.2020.00011 | 2020 IEEE International Conference on Knowledge Graph (ICKG) |
Keywords | DocType | ISBN |
Text Classification,Capsule Network,BiDynamic Routing,Self-Attention | Conference | 978-1-7281-8157-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Guo | 1 | 0 | 0.34 |
| Youquan Wang | 2 | 57 | 5.72 |
| Kaiyuan Gao | 3 | 0 | 0.34 |
| Jie Cao | 4 | 627 | 73.36 |
| Haicheng Tao | 5 | 13 | 2.91 |
| Chaoyue Chen | 6 | 0 | 0.34 |