Arzoo Katiyar
- Artificial Intelligence top 2%
- Topic Modeling 9
- Natural Language Processing Techniques 6
- Domain Adaptation and Few-Shot Learning 4
- Sentiment Analysis and Opinion Mining 3
- Advanced Text Analysis Techniques 2
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- Data Quality and Management 1
- Information Systems top 10%
- General Social Sciences top 10%
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- Automated Road and Building Extraction 1
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- Geographic Information Systems Studies 1
- Co-authors
- Claire CardieYi YangKilian Q. WeinbergerTianyi ZhangFelix WuYoav ArtziRebecca J. PassonneauRui Zhang
- Cited by
- Artificial IntelligenceManagement Science and Operations ResearchComputer Vision and Pattern Recognition
- Journals
- Theory and applications of categories (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (1 paper)
- Partner nations
- United StatesIndia
In The Last Decade
Arzoo Katiyar
11 papers receiving 699 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 705
- Management Science and Operations Research 98
- Computer Vision and Pattern Recognition 77
- Information Systems 77
- General Social Sciences 9
Countries citing papers authored by Arzoo Katiyar
This map shows the geographic impact of Arzoo Katiyar'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 Arzoo Katiyar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arzoo Katiyar more than expected).
Fields of papers citing papers by Arzoo Katiyar
This network shows the impact of papers produced by Arzoo Katiyar. 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 Arzoo Katiyar. The network helps show where Arzoo Katiyar may publish in the future.
Co-authorship network
The 18 scholars most cited alongside Arzoo Katiyar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 94 | |
| 2 | Revisiting Few-sample BERT Fine-tuning | 2021 | 126 |
| 3 | 2021 | 1 | |
| 4 | 2020 | 115 | |
| 5 | 2019 | 1 | |
| 6 | 2018 | 153 | |
| 7 | 2017 | 159 | |
| 8 | 2016 | 68 | |
| 9 | Cornell Belief and Sentiment System at TAC 2016. | 2016 | 3 |
| 10 | 2015 | 19 | |
| 11 | 2014 | 3 |
About Arzoo Katiyar
Arzoo Katiyar is a scholar working on Artificial Intelligence, Geography, Planning and Development and Signal Processing, having authored 11 papers that have together received 742 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers), Domain Adaptation and Few-Shot Learning (4 papers), Sentiment Analysis and Opinion Mining (3 papers), Advanced Text Analysis Techniques (2 papers), Data Quality and Management (1 paper), Automated Road and Building Extraction (1 paper) and Geographic Information Systems Studies (1 paper). The work is most often cited by research in Artificial Intelligence (705 citations), Management Science and Operations Research (98 citations) and Computer Vision and Pattern Recognition (77 citations). Arzoo Katiyar has collaborated with scholars based in United States and India. Frequent co-authors include Claire Cardie, Yi Yang, Kilian Q. Weinberger, Tianyi Zhang, Felix Wu, Yoav Artzi, Rebecca J. Passonneau, Rui Zhang, Joonsuk Park and Bishan Yang. Their work appears in journals such as Theory and applications of categories, arXiv (Cornell University) and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
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.