Kais Dukes
- Artificial Intelligence top 5%
- Natural Language Processing Techniques 10
- Topic Modeling 9
- Advanced Text Analysis Techniques 3
- Speech and dialogue systems 2
- Text and Document Classification Technologies 2
- Semantic Web and Ontologies 2
- Language and Linguistics top 10%
- General Social Sciences top 10%
- Information Systems top 10%
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- Historical and Linguistic Studies 2
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- Handwritten Text Recognition Techniques 1
- Journals
- Language Resources and Evaluation (5 papers)White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (2 papers)Recent Advances in Natural Language Processing (1 paper)
- Partner nations
- United KingdomUnited States
In The Last Decade
Kais Dukes
11 papers receiving 245 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 237
- Language and Linguistics 24
- General Social Sciences 7
- Information Systems 45
- Computer Science Applications 7
Countries citing papers authored by Kais Dukes
This map shows the geographic impact of Kais Dukes'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 Kais Dukes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kais Dukes more than expected).
Fields of papers citing papers by Kais Dukes
This network shows the impact of papers produced by Kais Dukes. 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 Kais Dukes. The network helps show where Kais Dukes may publish in the future.
Co-authorship network
The 5 scholars most cited alongside Kais Dukes, 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 | 2014 | 14 | |
| 2 | 2014 | 6 | |
| 3 | Unifying linguistic annotations and ontologies for the Arabic Quran | 2013 | 2 |
| 4 | LAMP: A Multimodal Web Platform for Collaborative Linguistic Analysis | 2012 | 9 |
| 5 | One-Step Statistical Parsing of Hybrid Dependency-Constituency Syntactic Representations | 2011 | 3 |
| 6 | 2011 | 39 | |
| 7 | 2011 | 56 | |
| 8 | Syntactic Annotation Guidelines for the Quranic Arabic Dependency Treebank. | 2010 | 28 |
| 9 | 2010 | 78 | |
| 10 | A Dependency Treebank of the Quran using traditional Arabic grammar | 2010 | 43 |
| 11 | LOGICON: A System for Extracting Semantic Structure using Partial Parsing | 2009 | 3 |
About Kais Dukes
Kais Dukes is a scholar working on Artificial Intelligence, Computer Science Applications and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 281 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (10 papers), Topic Modeling (9 papers), Advanced Text Analysis Techniques (3 papers), Historical and Linguistic Studies (2 papers), Speech and dialogue systems (2 papers), Text and Document Classification Technologies (2 papers), Semantic Web and Ontologies (2 papers) and Handwritten Text Recognition Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (237 citations), Language and Linguistics (24 citations) and General Social Sciences (7 citations). Kais Dukes has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Nizar Habash, Eric Atwell, Tim Buckwalter, Majdi Sawalha and Wajdi Zaghouani. Their work appears in journals such as Language Resources and Evaluation, White Rose Research Online (University of Leeds, The University of Sheffield, University of York) and Recent Advances in 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.