Niraj Aswani
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Semantic Web and Ontologies
- Advanced Text Analysis Techniques
- Sentiment Analysis and Opinion Mining
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- Web Data Mining and Analysis
Papers in
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- Natural Language Processing Techniques 6
- Topic Modeling 6
- Authorship Attribution and Profiling 2
- Text and Document Classification Technologies 2
- Speech Recognition and Synthesis 2
- Advanced Text Analysis Techniques 1
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- Spam and Phishing Detection 2
- Co-authors
- Kalina Bontcheva (5 shared papers)Robert Gaizauskas (4 shared papers)Leon Derczynski (1 shared paper)Diana Maynard (1 shared paper)Hamish Cunningham (2 shared papers)Ian Roberts (2 shared papers)Valentin Tablan (2 shared papers)Genevieve Gorrell (1 shared paper)
- Journals
- Language Resources and Evaluation (3 papers)Figshare (1 paper)
- Partner nations
- United Kingdom
In The Last Decade
Niraj Aswani
9 papers receiving 168 citations
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 149
- Information Systems 40
- Health Informatics 2
- Management Science and Operations Research 15
- Communication 8
Countries citing papers authored by Niraj Aswani
This map shows the geographic impact of Niraj Aswani'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 Niraj Aswani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Niraj Aswani more than expected).
Fields of papers citing papers by Niraj Aswani
This network shows the impact of papers produced by Niraj Aswani. 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 Niraj Aswani. The network helps show where Niraj Aswani may publish in the future.
Co-authors
The 11 scholars most cited alongside Niraj Aswani, 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 | 2013 | 69 | |
| 2 | 2013 | 60 | |
| 3 | 2008 | 16 | |
| 4 | 2005 | 14 | |
| 5 | Developing Morphological Analysers for South Asian Languages: Experimenting with the Hindi and Gujarati Languages | 2010 | 12 |
| 6 | 2005 | 8 | |
| 7 | English-Hindi Transliteration using Multiple Similarity Metrics | 2010 | 4 |
| 8 | Reputation Profiling with GATE. | 2012 | 4 |
| 9 | Reputation Proling with GATE | 2012 | 1 |
About Niraj Aswani
Niraj Aswani is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Sociology and Political Science and Management of Technology and Innovation, having authored 9 papers that have together received 188 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Topic Modeling (6 papers), Spam and Phishing Detection (2 papers), Authorship Attribution and Profiling (2 papers), Text and Document Classification Technologies (2 papers), Speech Recognition and Synthesis (2 papers), Advanced Text Analysis Techniques (1 paper) and Intellectual Property and Patents (1 paper). The work is most often cited by research in Artificial Intelligence (149 citations), Information Systems (40 citations), Health Informatics (2 citations), Management Science and Operations Research (15 citations) and Communication (8 citations). Niraj Aswani has collaborated with scholars based in United Kingdom. Frequent co-authors include Kalina Bontcheva, Robert Gaizauskas, Leon Derczynski, Diana Maynard, Hamish Cunningham, Ian Roberts, Valentin Tablan, Genevieve Gorrell, Angus Roberts and Mark Greenwood. Their work appears in journals such as Language Resources and Evaluation and Figshare.
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.