Manoj Pooleery
- Artificial Intelligence top 5%
- Information Systems top 10%
- Computer Vision and Pattern Recognition
- Language and Linguistics top 10%
- Sociology and Political Science
- Co-authors
- Owen RambowMona DiabNizar HabashRyan M. RothMohamed Al-BadrashinyAhmed El KholyRamy EskanderLeon Wu
- Topics
- EEG and Brain-Computer Interfaces (2 papers)Natural Language Processing Techniques (2 papers)Blind Source Separation Techniques (2 papers)
- Journals
- Language Resources and EvaluationColumbia Academic Commons (Columbia University)International Joint Conference on Natural Language Processing
- Partner nations
- United States
In The Last Decade
Manoj Pooleery
4 papers receiving 390 citations
Hit Papers
Peers
Comparison fields: 5 of 39
- Artificial Intelligence 401
- Information Systems 48
- Computer Vision and Pattern Recognition 43
- Language and Linguistics 36
- Sociology and Political Science 15
Countries citing papers authored by Manoj Pooleery
This map shows the geographic impact of Manoj Pooleery'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 Manoj Pooleery with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manoj Pooleery more than expected).
Fields of papers citing papers by Manoj Pooleery
This network shows the impact of papers produced by Manoj Pooleery. 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 Manoj Pooleery. The network helps show where Manoj Pooleery may publish in the future.
Co-authorship network of co-authors of Manoj Pooleery
This figure shows the co-authorship network connecting the top 25 collaborators of Manoj Pooleery. A scholar is included among the top collaborators of Manoj Pooleery 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 Manoj Pooleery. Manoj Pooleery is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabicbreakdown → | 414 |
| 2 | DIRA: Dialectal Arabic Information Retrieval Assistant | 6 |
| 3 | 0 | |
| 4 | 10 | |
| 5 | 2 |
About Manoj Pooleery
Manoj Pooleery is a scholar working on Signal Processing, Cognitive Neuroscience and Artificial Intelligence, having authored 5 papers that have together received 432 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (2 papers), Natural Language Processing Techniques (2 papers) and Blind Source Separation Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (401 citations), Language and Linguistics (36 citations) and Information Systems (48 citations). Manoj Pooleery has collaborated with scholars based in United States. Frequent co-authors include Owen Rambow, Mona Diab, Nizar Habash, Ryan M. Roth, Mohamed Al-Badrashiny, Ahmed El Kholy, Ramy Eskander, Leon Wu, David L. Waltz and Albert Boulanger. Their work appears in journals such as Language Resources and Evaluation, Columbia Academic Commons (Columbia University) 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.