Debanjan Ghosh
- Artificial Intelligence top 5%
- Information Systems top 5%
- Control and Systems Engineering top 10%
- Computer Networks and Communications top 10%
- Electrical and Electronic Engineering
- Co-authors
- Smaranda MuresanShambhu UpadhyayaRaj SharmanHengyi RaoPrashant MhaskarWeiwei GuoNina WacholderAlexander R. Fabbri
- Topics
- Topic Modeling (13 papers)Natural Language Processing Techniques (10 papers)Sentiment Analysis and Opinion Mining (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaIndustrial & Engineering Chemistry ResearchDecision Support Systems
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Debanjan Ghosh
30 papers receiving 571 citations
Peers
Comparison fields: 5 of 97
- Artificial Intelligence 377
- Information Systems 126
- Control and Systems Engineering 99
- Computer Networks and Communications 87
- Electrical and Electronic Engineering 37
Countries citing papers authored by Debanjan Ghosh
This map shows the geographic impact of Debanjan Ghosh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Debanjan Ghosh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debanjan Ghosh more than expected).
Fields of papers citing papers by Debanjan Ghosh
This network shows the impact of papers produced by Debanjan Ghosh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Debanjan Ghosh. The network helps show where Debanjan Ghosh may publish in the future.
Co-authorship network of co-authors of Debanjan Ghosh
This figure shows the co-authorship network connecting the top 25 collaborators of Debanjan Ghosh. A scholar is included among the top collaborators of Debanjan Ghosh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Debanjan Ghosh. Debanjan Ghosh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 9 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 1 | |
| 9 | 19 | |
| 10 | 2 | |
| 11 | 3 | |
| 12 | 20 | |
| 13 | 10 | |
| 14 | 8 | |
| 15 | 61 | |
| 16 | 2 | |
| 17 | Relation Classification using Entity Sequence Kernels | 2 |
| 18 | Using Sequence Kernels to identify Opinion Entities in Urdu | 5 |
| 19 | Using Cross-Lingual Projections to Generate Semantic Role Labeled Annotated Corpus for Urdu - A Resource Poor Language | 6 |
| 20 | 169 |
About Debanjan Ghosh
Debanjan Ghosh is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 33 papers that have together received 624 indexed citations. Recurring topics across this work include Topic Modeling (13 papers), Natural Language Processing Techniques (10 papers) and Sentiment Analysis and Opinion Mining (6 papers). The work is most often cited by research in Artificial Intelligence (377 citations), Software (24 citations) and Information Systems (126 citations). Debanjan Ghosh has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Smaranda Muresan, Shambhu Upadhyaya, Raj Sharman, Hengyi Rao, Prashant Mhaskar, Weiwei Guo, Nina Wacholder, Alexander R. Fabbri, Mark Aakhus and Rajeev K. Goel. Their work appears in journals such as SHILAP Revista de lepidopterología, Industrial & Engineering Chemistry Research and Decision Support Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.