Kashyap Popat
- Artificial Intelligence top 5%
- Sociology and Political Science top 5%
- Information Systems top 5%
- Signal Processing top 10%
- Statistical and Nonlinear Physics top 10%
- Co-authors
- Gerhard WeikumSubhabrata MukherjeeJannik StrötgenAndrew YatesPushpak BhattacharyyaSebastian RiedelFabio PetroniNicola De Cao
- Topics
- Topic Modeling (14 papers)Natural Language Processing Techniques (7 papers)Spam and Phishing Detection (5 papers)
- Journals
- Transactions of the Association for Computational LinguisticsMeeting of the Association for Computational LinguisticsInternational Joint Conference on Natural Language Processing
- Partner nations
- GermanyUnited StatesIndia
In The Last Decade
Kashyap Popat
14 papers receiving 538 citations
Peers
Comparison fields: 5 of 44
- Artificial Intelligence 418
- Sociology and Political Science 396
- Information Systems 253
- Signal Processing 62
- Statistical and Nonlinear Physics 49
Countries citing papers authored by Kashyap Popat
This map shows the geographic impact of Kashyap Popat'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 Kashyap Popat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kashyap Popat more than expected).
Fields of papers citing papers by Kashyap Popat
This network shows the impact of papers produced by Kashyap Popat. 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 Kashyap Popat. The network helps show where Kashyap Popat may publish in the future.
Co-authorship network of co-authors of Kashyap Popat
This figure shows the co-authorship network connecting the top 25 collaborators of Kashyap Popat. A scholar is included among the top collaborators of Kashyap Popat 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 Kashyap Popat. Kashyap Popat 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 | 50 | |
| 3 | 5 | |
| 4 | 22 | |
| 5 | 170 | |
| 6 | 26 | |
| 7 | 119 | |
| 8 | 36 | |
| 9 | 94 | |
| 10 | 8 | |
| 11 | The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis | 18 |
| 12 | Making Headlines in Hindi: Automatic English to Hindi News Headline Translation | 2 |
| 13 | Word Clustering for Data Sparsity: A Literature Survey | 1 |
| 14 | 18 |
About Kashyap Popat
Kashyap Popat is a scholar working on Artificial Intelligence, Information Systems and Sociology and Political Science, having authored 14 papers that have together received 570 indexed citations. Recurring topics across this work include Topic Modeling (14 papers), Natural Language Processing Techniques (7 papers) and Spam and Phishing Detection (5 papers). The work is most often cited by research in Artificial Intelligence (418 citations), Information Systems (253 citations) and Sociology and Political Science (396 citations). Kashyap Popat has collaborated with scholars based in Germany, United States and India. Frequent co-authors include Gerhard Weikum, Subhabrata Mukherjee, Jannik Strötgen, Andrew Yates, Pushpak Bhattacharyya, Sebastian Riedel, Fabio Petroni, Nicola De Cao, Ledell Wu and Naman Goyal. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Meeting of the Association for Computational Linguistics and International Joint Conference on 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.