Name
Abstract | ||
|---|---|---|
AbstractCharacter-based and word-based methods are two different solutions for Chinese word segmentation, the former exploiting sequence labeling models over characters and the latter using word-level features. Neural models have been exploited for character-based Chinese word segmentation, giving high accuracies by making use of external character embeddings, yet requiring less feature engineering. In this paper, we study a neural model for word-based Chinese word segmentation, by replacing the manually-designed discrete features with neural features in a transition-based word segmentation framework. Experimental results demonstrate that word features lead to comparable performance to the best systems in the literature, and a further combination of discrete and neural features obtains top accuracies on several benchmarks. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1613/jair.1.11266 | Hosted Content |
Field | DocType | Volume |
Text segmentation,Speech recognition,Artificial intelligence,Mathematics,Machine learning | Journal | 63 |
Issue | ISSN | Citations |
1 | 1076-9757 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meishan Zhang | 1 | 221 | 20.36 |
| Yue Zhang | 2 | 1364 | 114.17 |
| Guohong Fu | 3 | 192 | 28.22 |