Name
Title | ||
|---|---|---|
Automatic Topic Labeling model with Paired-Attention based on Pre-trained Deep Neural Network |
Abstract | ||
|---|---|---|
The automatic topic labeling model aims at generating a sound, interpretable, and meaningful topic label that is used to interpret an LDA-style discovered topic, intending to reduce the cognitive load of end-users while browsing or investigating the topics. In this study, we first introduced the pre-trained language model BERT to topic labeling tasks. It exploits the contextual embedding of the pr... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534093 | 2021 International Joint Conference on Neural Networks (IJCNN) |
Keywords | DocType | ISSN |
Deep learning,Neural networks,Feature extraction,Information filters,Encoding,Labeling,Resource management | Conference | 2161-4393 |
ISBN | Citations | PageRank |
978-1-6654-3900-8 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongbin He | 1 | 0 | 0.68 |
| Yanzhao Ren | 2 | 0 | 1.01 |
| Abdul Mateen Khattak | 3 | 0 | 1.01 |
| Xinliang Liu | 4 | 0 | 0.34 |
| Sha Tao | 5 | 0 | 0.68 |
| Wanlin Gao | 6 | 6 | 7.58 |