Manisha Verma
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Co-authors
- Arun Kumar YadavNeelam BhardwajNick CraswellEmine YılmazDebasis GangulyFilip RadlinskiLiangzhi LiHajime Nagahara
- Topics
- Topic Modeling (10 papers)Information Retrieval and Search Behavior (10 papers)Advanced Text Analysis Techniques (8 papers)
- Journals
- Applied Intelligence2021 IEEE/CVF International Conference on Computer Vision (ICCV)Data Archiving and Networked Services (DANS)
- Partner nations
- United StatesUnited KingdomIndia
In The Last Decade
Manisha Verma
25 papers receiving 392 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 220
- Information Systems 171
- Computer Networks and Communications 101
- Computer Vision and Pattern Recognition 84
- Electrical and Electronic Engineering 38
Countries citing papers authored by Manisha Verma
This map shows the geographic impact of Manisha Verma'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 Manisha Verma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manisha Verma more than expected).
Fields of papers citing papers by Manisha Verma
This network shows the impact of papers produced by Manisha Verma. 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 Manisha Verma. The network helps show where Manisha Verma may publish in the future.
Co-authorship network of co-authors of Manisha Verma
This figure shows the co-authorship network connecting the top 25 collaborators of Manisha Verma. A scholar is included among the top collaborators of Manisha Verma 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 Manisha Verma. Manisha Verma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 23 | |
| 3 | 31 | |
| 4 | 0 | |
| 5 | 23 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 14 | |
| 9 | 6 | |
| 10 | 2 | |
| 11 | TREC Complex Answer Retrieval Overview. | 39 |
| 12 | 2 | |
| 13 | Overview of the TREC Tasks Track 2016. | 3 |
| 14 | 101 | |
| 15 | 17 | |
| 16 | Overview of the TREC 2015 Tasks Track. | 8 |
| 17 | Task-Based User Modelling for Personalization via Probabilistic Matrix Factorization. | 4 |
| 18 | 0 | |
| 19 | 50 | |
| 20 | 7 |
About Manisha Verma
Manisha Verma is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition, having authored 28 papers that have together received 410 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Information Retrieval and Search Behavior (10 papers) and Advanced Text Analysis Techniques (8 papers). The work is most often cited by research in Information Systems (171 citations), Artificial Intelligence (220 citations) and Computer Networks and Communications (101 citations). Manisha Verma has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Arun Kumar Yadav, Neelam Bhardwaj, Nick Craswell, Emine Yılmaz, Debasis Ganguly, Filip Radlinski, Liangzhi Li, Hajime Nagahara, Bowen Wang and Yuta Nakashima. Their work appears in journals such as Applied Intelligence, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and Data Archiving and Networked Services (DANS).
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.