Name
Abstract | ||
|---|---|---|
Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. This paper presents a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/P17-2096 | PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2 |
DocType | Volume | Citations |
Conference | abs/1704.07047 | 20 |
PageRank | References | Authors |
0.75 | 32 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deng Cai | 1 | 67 | 5.96 |
| Hai Zhao | 2 | 960 | 113.64 |
| Zhisong Zhang | 3 | 37 | 2.00 |
| Yuan Xin | 4 | 26 | 1.20 |
| Yongjian Wu | 5 | 24 | 3.49 |
| Feiyue Huang | 6 | 226 | 41.86 |