Name
Abstract | ||
|---|---|---|
In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Besides, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation. The data set is manually annotated with Universal Dependencies. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/e17-2052 | EACL |
DocType | Volume | Citations |
Conference | abs/1703.10772 | 0 |
PageRank | References | Authors |
0.34 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Irshad Ahmad Bhat | 1 | 4 | 2.11 |
| Riyaz Ahmad Bhat | 2 | 16 | 7.65 |
| Manish Shrivastava | 3 | 19 | 23.49 |
| Dipti Misra Sharma | 4 | 262 | 45.90 |