Rahul Bhagat
- Artificial Intelligence top 5%
- Information Systems top 10%
- Computer Vision and Pattern Recognition
- Molecular Biology
- Management Science and Operations Research
- Co-authors
- Eduard HovyDeepak RavichandranPatrick PantelFernando PereiraMarius PaşcaPartha TalukdarJoseph ReisingerTimothy Chklovski
- Topics
- Topic Modeling (11 papers)Natural Language Processing Techniques (10 papers)Speech and dialogue systems (2 papers)
- Journals
- SHILAP Revista de lepidopterologíaComputational LinguisticsEmpirical Methods in Natural Language Processing
- Partner nations
- United StatesIndiaGermany
In The Last Decade
Rahul Bhagat
12 papers receiving 366 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 380
- Information Systems 55
- Computer Vision and Pattern Recognition 35
- Molecular Biology 27
- Management Science and Operations Research 21
Countries citing papers authored by Rahul Bhagat
This map shows the geographic impact of Rahul Bhagat'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 Rahul Bhagat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rahul Bhagat more than expected).
Fields of papers citing papers by Rahul Bhagat
This network shows the impact of papers produced by Rahul Bhagat. 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 Rahul Bhagat. The network helps show where Rahul Bhagat may publish in the future.
Co-authorship network of co-authors of Rahul Bhagat
This figure shows the co-authorship network connecting the top 25 collaborators of Rahul Bhagat. A scholar is included among the top collaborators of Rahul Bhagat 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 Rahul Bhagat. Rahul Bhagat is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | Introducing Virtual Law offices in the Existing Judiciary | 1 |
| 5 | 96 | |
| 6 | 11 | |
| 7 | Learning paraphrases from text | 11 |
| 8 | Large Scale Acquisition of Paraphrases for Learning Surface Patterns | 67 |
| 9 | 93 | |
| 10 | Phonetic models for generating spelling variants | 11 |
| 11 | ISP: Learning Inferential Selectional Preferences | 66 |
| 12 | LEDIR: An Unsupervised Algorithm for Learning Directionality of Inference Rules | 43 |
| 13 | 8 | |
| 14 | 2 |
About Rahul Bhagat
Rahul Bhagat is a scholar working on Artificial Intelligence, Computer Science Applications and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 412 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Natural Language Processing Techniques (10 papers) and Speech and dialogue systems (2 papers). The work is most often cited by research in Artificial Intelligence (380 citations), Computational Mathematics (2 citations) and Information Systems (55 citations). Rahul Bhagat has collaborated with scholars based in United States, India and Germany. Frequent co-authors include Eduard Hovy, Deepak Ravichandran, Patrick Pantel, Fernando Pereira, Marius Paşca, Partha Talukdar, Joseph Reisinger, Timothy Chklovski, Bonaventura Coppola and Siddharth Patwardhan. Their work appears in journals such as SHILAP Revista de lepidopterología, Computational Linguistics and Empirical Methods in Natural Language Processing.
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.