Arnav Sharma
Research Subjects
Research Interests
Arnav Sharma has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
Dipti M. Sharma
Kohli Center on Intelligent Systems (KCIS) International Institute of Information Technology IIIT Hyderabad) Gachibowli 500032 Hyderabad Hyderabad Telangana
1 shared publication- Manish Shrivastava
International Institute of Information Technology Hyderabad
1 shared publication - Piyush Bansal
Kohli Center on Intelligent Systems (KCIS) International Institute of Information Technology IIIT Hyderabad) Gachibowli 500032 Hyderabad Hyderabad Telangana
1 shared publication - Radhika Mamidi
International Institute of Information Technology Hyderabad India
1 shared publication - Raveesh Motlani
Kohli Center on Intelligent Systems (KCIS) International Institute of Information Technology IIIT Hyderabad) Gachibowli 500032 Hyderabad Hyderabad Telangana
1 shared publication - Sakshi Gupta
Kohli Center on Intelligent Systems (KCIS) International Institute of Information Technology IIIT Hyderabad) Gachibowli 500032 Hyderabad Hyderabad Telangana
1 shared publication
Latest Publications
- Open Access
Shallow Parsing Pipeline for Hindi-English Code-Mixed Social Media Text
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 http://bit.ly/csmt-parser-api .
Open AccessShallow Parsing Pipeline for Hindi-English Code-Mixed Social Media Text
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 http://bit.ly/csmt-parser-api .