Aakanksha Naik
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- Topic Modeling 10
- Natural Language Processing Techniques 8
- Semantic Web and Ontologies 3
- Advanced Text Analysis Techniques 3
- Speech and dialogue systems 1
- Machine Learning in Healthcare 1
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- Biomedical Text Mining and Ontologies 3
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- Digital Humanities and Scholarship 1
- Co-authors
- Carolyn Penstein RoséAbhilasha RavichanderEduard HovyLucy Lu WangEric NybergSravanthi ParasaSergey FeldmanTom Hope
- Journals
- Transactions of the Association for Computational Linguistics (1 paper)Journal of Cardiac Failure (1 paper)eScholarship (California Digital Library) (1 paper)
- Partner nations
- United States
In The Last Decade
Aakanksha Naik
13 papers receiving 100 citations
Peers
Comparison fields: 5 of 33
- Health Informatics 6
- Artificial Intelligence 84
- Signal Processing 10
- Toxicology 3
- Health Information Management 3
Countries citing papers authored by Aakanksha Naik
This map shows the geographic impact of Aakanksha Naik'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 Aakanksha Naik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aakanksha Naik more than expected).
Fields of papers citing papers by Aakanksha Naik
This network shows the impact of papers produced by Aakanksha Naik. 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 Aakanksha Naik. The network helps show where Aakanksha Naik may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Aakanksha Naik, 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 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 3 | |
| 9 | 2024 | 9 | |
| 10 | 2023 | 0 | |
| 11 | 2022 | 2 | |
| 12 | 2022 | 14 | |
| 13 | 2021 | 7 | |
| 14 | 2019 | 25 | |
| 15 | 2019 | 19 | |
| 16 | 2019 | 4 | |
| 17 | 2017 | 1 | |
| 18 | 2017 | 2 | |
| 19 | 2017 | 16 |
About Aakanksha Naik
Aakanksha Naik is a scholar working on Health Informatics, Artificial Intelligence, Communication, Signal Processing and Literature and Literary Theory, having authored 19 papers that have together received 104 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (8 papers), Biomedical Text Mining and Ontologies (3 papers), Semantic Web and Ontologies (3 papers), Advanced Text Analysis Techniques (3 papers), Speech and dialogue systems (1 paper), Digital Humanities and Scholarship (1 paper) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Health Informatics (6 citations), Artificial Intelligence (84 citations), Signal Processing (10 citations), Toxicology (3 citations) and Health Information Management (3 citations). Aakanksha Naik has collaborated with scholars based in United States. Frequent co-authors include Carolyn Penstein Rosé, Abhilasha Ravichander, Eduard Hovy, Lucy Lu Wang, Eric Nyberg, Sravanthi Parasa, Sergey Feldman, Tom Hope, Khyathi Raghavi Chandu and Maria Antoniak. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Journal of Cardiac Failure and eScholarship (California Digital Library).
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.