Name
Title | ||
|---|---|---|
Improving Short Text Classification Using Context-Sensitive Representations and Content-Aware Extended Topic Knowledge |
Abstract | ||
|---|---|---|
Most existing short text classification models suffer from poor performance because of the information sparsity of short texts and the polysemous class-bearing words. To alleviate these issues, we propose a context-sensitive topic memory network (cs-TMN) by learning context-sensitive text representations and content-aware extended topic knowledge. Different from TMN that utilizes context-independent word embedding and extended topic knowledge, we further employ context-sensitive word embedding, comprised of local context representation and global context representation to alleviate the polysemous issue. Besides, extended topic knowledge matched by context-sensitive word embedding is proven content-aware in comparison with previous works. Empirical results demonstrate the effectiveness of our cs-TMN, outperforming state-of-the-art models on short text classification on four public datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-75765-6_8 | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2021, PT II |
Keywords | DocType | Volume |
Short text classification, Context-sensitive text representations, Topic knowledge | Conference | 12713 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhihao Ye | 1 | 0 | 0.34 |
| Rui Wen | 2 | 20 | 5.13 |
| Xi Chen | 3 | 35 | 8.36 |
| Ye Liu | 4 | 0 | 0.34 |
| Ziheng Zhang | 5 | 0 | 2.03 |
| Zhiyong Li | 6 | 64 | 11.15 |
| Ke Nai | 7 | 12 | 2.90 |
| Yefeng Zheng | 8 | 1391 | 114.67 |