Name
Abstract | ||
|---|---|---|
In this study, the problem of shallow parsing of Hindi-English code-mixed social media text (CSMT) has been addressed. We have annotated the data, developed a language identifier, a normalizer, a part-of-speech tagger and a shallow parser. To the best of our knowledge, we are the first to attempt shallow parsing on CSMT. The pipeline developed has been made available to the research community with the goal of enabling better text analysis of Hindi English CSMT. The pipeline is accessible at 1 . |
Year | DOI | Venue |
|---|---|---|
2016 | 10.18653/v1/N16-1159 | HLT-NAACL |
Field | DocType | Volume |
Shallow parsing,Social media,Identifier,Hindi,Computer science,Bottom-up parsing,Artificial intelligence,Natural language processing,Parsing | Conference | abs/1604.03136 |
Citations | PageRank | References |
3 | 0.43 | 11 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnav Sharma | 1 | 3 | 0.43 |
| Sakshi Gupta | 2 | 3 | 0.43 |
| Raveesh Motlani | 3 | 3 | 0.43 |
| Piyush Bansal | 4 | 5 | 1.12 |
| Manish Shrivastava | 5 | 19 | 23.49 |
| Radhika Mamidi | 6 | 9 | 6.85 |
| Dipti Misra Sharma | 7 | 262 | 45.90 |